The number of kids less than 6 years of age who experienced skull fractures in this country, since 2000.  Skull fractures in young children symbolize the results of many social problems that exist, ranging from lack of attentiveness to your young child, to unnecessary accident or injury, to neglect, to abuse.  With multiple causes. they may be unpreventable, but they can at least be reduced in number.  This social problem is one that health insurance can help to improve, but requires considerable change on behalf of society itself.  Most importantly however, is the fact that more than likely not one insurance company has reviewed the distribution of this problem in large areas, such as the state or regional level.  Too much time is spent bickering on thousands of unneccessary public health problems, like this one, due to in adequate care and service provided at the health insurance industry level.

How is it that the White House, congress, HMOs planners, and the bulk of the health insurance industry can lead us in circles?  With the establishment of the HMP concept in 1971, by Pres. Richard Nixon, the plans were to improve the health insurance for everyone in the United States.  However, health insurance coverage has always been a product made available mostly to the employed, in particular those who earn more and work for large companies.

Health insurance for small companies has never experienced an advantage over the past 45 years.  Nor have better programs evolved due to this initial development of Health Maintenance Organizations or HMOs.  Similar failures exist in nearly every one of the programs for health insurance that was approved and managed.  The programs with the least failures illustrate how we assign value to a new plan.  The plans with the least non-compliant insurance companies demonstrate a “win” for this system; the the plans with the greatest number of successes.

The inadequacies of government and health insurance industries are directly responsible for the societal dissatisfactions that current exist, and are one of the major contributing factors leading to the violence and life threatening behavioral problems that currently exist.  The Columbine incident serves as a primary example of this.  At first, we cannot clearly see how such a localized event may relate to this nation’s “personality.”  Poor health insurance is one of numerous triggers that impact how people behave and “feel” about life in general.  Differentiation of kids into the “rich and poor”, “have and have nots”  leads to psychological unrest, which in turn feed into whatever other social failures exist in a community setting.  This does not imply that insurance companies are the direct cause for these tragedies; but the lack of attention insurance companies pay to dealing with these kinds of social disruptions, and developing programs to treat them or tack action against them which is well targeted, is a consequence of their inability to spatially evaluate these important social/societal problems.

In the recent changes taking place in the nation’s health insurance “plans”, while a consider number of previously uninsured were subsequently enrolled, a large number of previously enrolled recipients lost their coverage.  Its like trading pewter for tin; one is more highly favored than the other–for now.

A major problem that persists due to this bickering and changing of plans is the customers–the “members” according to insurance companies, and “patients” according the health care providers–suffer as the consequence of governmental-corporate arguments.  When much of the time is spent by congress trying to develop yet another “new plan”, little or no time is spent repairing the damages that already exist.  In this case, these “damages” are the increasingly ill-health that the American people are demonstrating, to nearby health facilities and to insurance companies less and less willing to sponsor them, and facilitate a transition to a stabler, perhaps even healthier, state of living.

Adult sexual abuse is a major problem in this country, which can be directly related to the unwillingness of insurance companies to develop productive programs.  Their unwillingness to be required to cover individuals with chronic disease opens the door for refusing coverage to people with other medical problems, that appear to be “unphysical” at first.  But if the many socially defined conditions and behaviors have underlying genomic, physiological, endocrine, genetic, or epigenomic (impact of environment on our genetics expression), this makes it possible for these companies to refuse care to families with significant biologically, drug-abuse induced misbehaviors, including child and spouse abuse, or LGBTI-related health risks.   Is society ready for the gene argument to be used to explain why the insurance companies can refuse to cover anyone’s health care, for any given medical diagnosis reason?

This current decision being made about health care will delay the development of the overall health care system itself, due to the “control” and “power” insurance companies have on Congress, and White House politics and Congress have upon the development of governmentally funded healthcare coverage.  Remember, the issues currently at hand with the new Trump Health Insurance plan pertain to those who are in need of coverage, many in desperate need.  Illegal immigrants get disqualified for never having paid into the system, nor legally tried to become a part of the system.  (Its like joining a club without paying the dues–should other paying “members” let this continue?).  But the less engaged “innocent bystanders”, who are “victims of their health”, get punished the worst by this.

An abused child, malnourished by neglectful parents, may never receive the care required to reach his or her 18th year.  I produced this may “one generation of pre-school children ago” an applied a formula that emphasized the cities where this form of abuse was the greatest.  As noted earlier, the fact that insurance companies are unaware of this can be blamed on their disrespect for the public and their focus on increasing earnings, instead of health.  But the fact that the government too is engaged in this lack of respect or interest in public health enables these initial niduses for this new social epidemic to continue and spread into other communities.  It would be very easy to study these “hot spots” for malnutrition, study comparable areas without as much abuse, and determine the causes for this social problem. But the current system remains too focused on financial aspects to enable this part of the healthcare system to be improved.

The next steps taken by congress to disqualify people from health insurance coverage, or demand increase financial contributions on behalf of those that want it or need it, will ultimately reduced a system with lesser customers. (Obamacare disqualified the borderline poor who were disabled, impacting enrollment in Medicaid Medicare and Handicapped Disability programs by 8 to 10 percent in November 2015.)  The cost for care will become a deterrent to even the most commonly sick.

Back in 1970, my mother had to deal with the refusal of Blue Cross-Blue Shield (BCBS) to manage my epilepsy needs, which included an annual visit with the neurologist, coverage of the cost for my prescription drugs, my annual EEG, my CAT scan (then a new technology), my meetings with social workers and counselors, my occasional emergency or critical care visits.  When compared with the cost for my annual physical for school, BCBS covered less than 10% of my healthcare needs.  So why even enroll?

Imagine for a moment a society where those eligible for treatment due to drug abuse are only the employed, able to afford health insurance.  The amount of opioid abuse in this country could mean that 50% or more of those experiencing this problem will not be fully managed unless effective programs are put in place.  This means that insurance companies unwilling to cover you for this past history, also set the stage for your future demise, should such a habit become hard to break.  This also mean, the only surviving opioid abusers (with that inherited genome), could become the middle to upper class workers, able to afford insurance, able to afford the care for their abuse provided by the insurers.  The lower class (other genome bearers) may ultimately die off, allowing social Darwinism to become the deciding factor for how addicted the future generations of kids, produced by these workers, will influence society and its healthcare needs.

Another unfortunate set of conditions influenced by the transition of care that appears to be happening is the enhanced neglect of care provided to certain victims of human behavioral conditions.  Over the years, child abandonment has developed small hot spots or niduses within large urban settings.  The statistics I used to analyze this, for the most part, came from the pre-Detroit Failure, pre-BLM social disruption years. The impacts of poverty now may be accentuated in regions where poverty was already having an impact on the health of specific communities.  Interestingly, the Obamacare and Trumpcare systems both failed to address this growing problem in certain urban settings.  They perpetuated bad practices already developed just before the passage of PPACA, and initiated in pieces by the everchanging HMO plans since 1971.

This map again depicts the core spots where abuse and neglect lead to the abandonment of newborns by placing them in predefined spots (hospitals, police stations, firehouses) where they may be transferred to a reputable care facility.  These are the 2000 to 2010 stats on this newly evolved “diagnosis” entered into the medical records.  It would be interesting to see how much these cases have increased, and what new niduses were developed since 2010.

It is possible that the currently change in healthcare insurance program will, like the failures before it, also be short lived, assuming it is passed.  Theoretically, it is important to remember that congress is the cause for its passage, but Insurance Companies themselves the cause for its evolution into this state of failure that it is experiencing.

Insurance companies do have an unwritten social obligation, which they don’t recognize, to provide coverage for healthcare in as many ways possible, so as to reduce the rates of sickness and death that result from poor personal and professional health care management.  But when insurance programs direct their attention primarily to their investors and selves, as financial officers looking for success, they engage in a unique form of evolutionary selection, akin to an offshoot of the “Soylent Green” scenario.  You need to “feed” your consumers enough to keep them engaged, but prevent them from dying off completely — otherwise, you lose your need for existence.

Children abusing children–peak areas in this country.  Are there are programs devoted to this cause in these given areas?  Probably not.  I doubt anyone in the insurance business is familiar with this above 2010/2012 finding.

Like other social “diseases” or misbehaviors, insurance companies have spent minimal time trying to deal with behavioral health.  Their focus is on physical health, or conditions that can be successfully treated by basic physical means.  Like what was said earlier, society has given rise to a new form of physical disease due to the genomic and epigenomic philosophies.  Genetics somehow relates to our health, knowledge of the genome will allow for these relationships to be “uncovered” (or so they think), and by understanding human genetics makeup and gene expression, we can turn any social or psychological condition into a physical condition with potential “physical treatments”.

Other examples of Social Disease and the inability of insurance companies to manage their patient populations follow.

Homelessness in 2010 (V and E codes derived); new niduses have no doubt been developed.

Congenital Tuberculosis – peak areas influences through immigration and the lack of adequate testing and screening; kids are born with TB due to inadequate plans.  These are all cases, not just newborns (when they are diagnosed) or kids <18 yo, and so may represent some young adults as well, who were born here and had TB.

Beri beri – a disease of malnutrition and another sign of regional mismanagement by health insurance companies and governments.

Rickets – another disease malnutrition — this time CHRONIC!

Insurance companies and the past plans have done little to properly manage suicide.  In the last ten years, suicide by children and especially teens has become a priority according to many suicide programs.  Yet, it is likely none of these programs have reviewed the distributions of age-group related cases at the small area level.

Pedestrian accidents, which involve being hit by a car

 Based on V-codes, E-codes; another overlooked preventable status of care and life style.

Mapping all of the ICDs, V-codes and E-Codes requires an accelerated mapping program.  This program was actually developed between 2005 and 2010, tested, and used to produce these national maps.  Some studies were performed as well on multiple age-groups and cultural backgrounds.

In spite of the possibility of developing such a system, none of the major providers of EMR data use and integration have designed a program equivalent to this version that I developed.  To date, these companies include all of those engaged in EMR utilization, even the largest ones providing numerous agencies with EMR related BI and ERP support devoted the healthcare and Population Health.

An early test of my modeling algorithm.  In the first months, I developed the “Three-Tier” model for overlaying one set of conditions or features over another.  This was used to compare the distribution of two similar ICDs, one culturally-related, the other culturally-linked (genetically linked, but not culturally bound; I covered these types of diseases and their classification on several other pages).  

The goal of such programs should be to produce 1000s of map per day and design focused reporting tools on place, race, ethnicity, religion, language, culture, age range, country of origin, in-migration patterns, land use patterns, SES, genomics, epigenomics, etc.  The current system of health insurance is extremely inadequate for managing the complex health of an increasing complex US population.

The value of Medical GIS is the answer these problems, assuming it is properly employed.  Current products, because they require stepwise development and data development and process, are still inadequate for meeting these needs.  The government and health insurance programs, and their healthcare reform antics, are the primary cause for this delay in technological advancements.  We are repeatedly sidetracked by arguments focused on eligibility for health insurance coverage, not on population, community, family and personal health related needs.


My 2010 SAS spatial analysis 3D map -video of the U.S., predicting the hotspots for outbreaks due to lack of childhood immunization (posted numerous times since my study).  This technology combined a grid mapping algorithm I developed with a 3D modeling method that bypasses the need for SAS-GIS and other traditional GIS tools, considered costly by managed care companies and healthcare agencies or institutions.

We are in the midst of a crisis in managed care.  This crisis is due to the major  discrepancies in the definition of healthcare needs and the policies developed by the White House, by Congress, and by insurance companies, regarding how to best monitor local and national health and determine where changes need to be made.

The first reason for this current state of failure we are in is the inability of any one business, agency, institution, program or leading healthcare institution to effectively monitor the long term health of their patients, and thereby define the terms for future performance of this program.   If we refer for a moment to the 100th Monkey Effect, which states that once one business starts to engage in employing an innovative discovery, others will follow (see ).  The practice of health care is a culture that has become stagnant in recent decades.

Since  Feb. 17, 1971 (Pres. Richard Nixon, and Edgar Kaiser), the official definition of improved health care and ‘better managed care’ has been in the minds and speeches of numerous politicians.  None have made a progressive change that has endured and resulted in long lasting care, that improves over the years along with patient and population health.

This legislation plan was developed in response to the same barriers to effective healthcare that exist today, namely long surviving programs that still exist today, like Blue Cross Blue Shield (see CSPAN. 1971. Birth of the HMO.  Large healthcare insurance industries (most still existing today) were the first perpetrators of this prejudicial treatment, which specifically targeted the patients born with an illness or birth defect, the young and potentially lifetime disabled, the poor unable to afford a healthcare path to improved health, or the aged and ill who cost more and more each year to keep alive and healthy.

Surveillance of a local genomic condition

The result of this attitude about the sick is that the sick, and those who are healthy and not yet sick, can undergo numerous changes in healthcare coverage.  We are thus left with a population that we can primarily monitor on a year by year basis.  Few patients can be monitored for their entire life experience with healthcare.  This means prediction models have to be developed in a fairly segmented way.

Today, we rely upon population level historical health care data to evaluate groups of peoples with similar disease experiences.  We try to employ the group data for developing prediction models about the next patients future care may become, and more importantly, what forms of care will they need and what costs may be incurred?

To date, prediction modeling has also failed to implement several forms of new technology and techniques related to spatial analyses.  Zip code related analyses are often mentioned, but true spatial analysis and the development of spatial risk models (maps of risk, cause and effect, cost, etc) are lacking from the system.  Grid modeling relies upon square grids with 43% spatial uncertainty error, versus hexagonal grids which only produce 17% spatial uncertainty error (see my hexagonal grids pages).

Institutions are now barely capable of mapping their entire patient population, at the detailed level (points, not zip codes).  To spatially surveil and analyze their patients, they have to implement a system wide approach to GIS utilization and development.  It is possible to map all of that data obtained from EMR and better target care, better target the changes needed, and target and monitor specific audiences and people who are most in need of change.

The following technologies applicable to health surveillance were developed 10 and 17 years ago (two creations: grid mapping health, and then 3D mapping the health care population’s status).  This first posting details applications of GIS to surveillance for biological and ecological diseases; the next posting will be focused on culture, human behavior and healthcare concepts.


Disease Ecology 

The most traditional use of GIS for disease analysis is perhaps Disease Ecology related, or the evaluation of diseases linked to the environmental setting.  In recent months, the best example of this disease type is the Zika virus brought into the periurban settings of in-migration towns and cities.  Prior to Zika, there were the Chikungunya and West Nile disease, which like the Zika can be carried and introduced to humans via the mosquito vector.  The following is one of the first 3D rotating maps I produced of the US devoted to these ecological disease patterns.  It is based upon a programming tool I developed for use by systems that lack, cannot afford, or will not invest in a GIS.

An example of some early Ecological Disease mapping I applied my formulas to.  This is a zip code case count map with 3D used to display the number of cases for each long-lat linked to a zip code.  It was also used to experiment with the use of the rotating imagery, tilt and video generation protocols and formulas that I developed, to produce 3d mapping videos for every condition, finding, case, etc. that was analyzed, as part of a regular reporting tool.  This tool produced 24,000 or more maps per day, with 700 to 1000 figures used to produce each 360 degree spin of the United States.

Now it ends up, this methodology works many times faster than your standard GIS system.  At peak performance for one company, I was able to produce thousands of results per day; but mind you, this was produced using a Terradata workstation, analyzing between 40 million and 90 million (120 million some say) patients’ medical records (not claims) nationally; it took 20 minutes to run each program; an hour to perform a quality review (not including rerun if need be) and convert the outcomes into a video.

Hot Spots

Are there places where particular diseases appear to produce cases and/or exist in isolation from other potentially high risk populations?  Several such diseases were uncovered by mapping the majority of ICDs known to be rare in their distribution.  Louis Bar sydnrome (above) was one such example.  Hereditary Choroid Dystrophy of Children was another, and Werdung Hofmann Spinal Degradation the third with a highly localized dense disease pattern.

Hereditary Choroid Dystrophy

Werdung Hoffman Spinal Degeneration



Years ago, after reviewing most of the ICDs for my US patient population, and producing maps of these diseases related to specific environments, parts of the world and climates, I defined four types of relationships that exist between disease and culture.

The first is the cultural ecological link, resulting in cultural tendencies to be exposed to common environmental infectious, organismal, and zoonotic disease patterns.

An organismal disease

The second are culturally-bound–they are conditions, usually psychological, that exist mostly because the cultural way of being and thinking think they exist.   They are most often treated for based upon the western medicine psychiatric diagnoses assigned.

The third kind of disease or condition linked to culture are cultural human behavior types, which related to foodways, personal cleaning habits, person living habits and behaviors based of socially predefined expectations.  Infibulation is the best example of this–it is neither necessary nor essential to life, but important to [sometimes] personal, social and community life requirements, and often has negative health related effects linked to infection, promiscuity problems, and relationship/family maintenance behaviors.

The Muslim practice of Infibulation or Female Genital Mutilation, based upon an N-N2 technique later described on this page. 

The third type are also commonly related to western culture diseases like diabetes and heart disease.  It is possible that some cases of Epilepsy, due to their similarities with other cultural-bound syndromes, may have cultural influences and in some cases be both physiological and western-culturally influenced in their patterns.

The fourth pattern of diseases related to cultural are culturally-linked, and are somehow physically linked to a culturally defined group of people, like a culture and genomic conditions, are as follows.  Each has possible genetic or genomic related causes, which are somehow exacerbated or facilitated in their genomic expression, due to cultural and sociocultural behavioral patterns.:

Takayasu is a congenital-, developmental-, and probably genomic-related disease linked to the maldevelopment (significant narrowing or stricture development) of blood vessels by the heart.  The genomic and epigenomic aspects of this condition have not been explored.  This condition is most linked to Hawaiian borne families, but may have other international genomic characteristics as well.  This spatial model used zip code counts, suggesting in-migration regions where people from this cultural group, with this condition, tend to aggregate.


Obscure African Cardiomyopathy is a heart condition that is frequent missed and/or uncoded in the patients’ records.  The underlying problem, cardiomyopathy, already has a specific code that may be linked to claims for billing purposes.  The related mostly African condition is therefore not entered in many places, since to specific billing actions may be linked just to this condition.  It is therefore detectable by the nature and amount of care provided to these patients, which hints at two potential Spatial Analysis Epidemiology research questions:  do African Americans with this condition have specific genomic patterns? and do they receive a different amount and form of care unknowingly than African Americans with traditional cardiomyopathy onset, linked to the more common heart disease conditions throughout our population?


Until recently, with the development of more useful EMRs, the limiter to developing decent spatial models has been the limits of the availability of useful data, the knowledge of its potential users, and especiually the knowledge of the value of these uses by upper level management.

In recent years, managed care programs have turned to looking at ways to develop prediction models, for cost modeling and population health modeling purposes. The development of GIS or geographic information systems has made it possible for place to be related to illness, leaving it up to the observers of any findings to add place and spatial analysis techniques to their review if the cause and effect of health and disease.  In the genomic world of study, we define the causes for diseases within individuals upon their genetic make up.   The counterapproach to this is using an epigenomics approach, where we look at how objects and events that occur outside the body relate in turn to the body itself (focusing on its genetics that are produced due to these “environmental stimuli”).  These features are then related to disease, disease producing (inducing) living patterns, disease producing (inducing) behaviors, or the initiation of a disease development process through stimulating the expression of a gene.

In healthcare, GIS enables us to make the best use of our EMR data.  GIS may also be applied to other managed care processes, such as financial analysis (probably already being done).  GIS enables us to better understand how the environment and epigenomics have an impact on disease, human ecology (the relationship between living spaces and health) and human behaviors.

The following are more examples of its application.

Human Ecology

Pinta is a skin diagnosis induced by an organism native to certain parts of Mexico–Treponema carateum.  It is also an indicator of the cultural movement of people who carry it across the United States.  Genetic studies of the organism and the different clinical expression of the disease may be linked either to the genetics of the immigrants bringing it to the U.S. and/or the strain of Treponema.  This particular video was used to display the effects of zooming in and out of a hot spot for the condition, as part of a surveillance program.

The most basic examples of spatially understandable diseases are the natural ecological induced zoonotic diseases, the vector-host driven ecological/human ecological diseases, the human or cultural behavioral induced diseases, and the pure culturally-bred, culturally-bound disease conditions.

To evaluate this aspect of human disease ecology, I identified and evaluated the distributions of well over 100 diseases, with their vector and or host natural history linked to specific continents or regions of continents.  Evaluating an EMR representing approximately 50-75M US patients across the country, the following distribution map video was developed:

Foreign Zoonotic Disease


Tick-borne diseases – local endemic forms and forms from various parts of the world.


Regionalism is a sensitive word.  It assigns, and to some blames, a given region for a given public health problem.  Regional disease patterns can be looked at ecologically, geographically, zoogeographically, climatically, and sociodemographic or behaviorally.  For example, in the mid 1800s, chiggers were common to eastern European homesteads due to sanitation practices, climate, the organisms’ living requirements, and, strangely, the human hair styles (long hair).  The long hair and dreadlocks habits of slavic cultures made them more suceptible to this, what was then defined as a “regional disease pattern.”

Reviewing the ICD 9s, I was able to related certain diseases to certain countries, and produce US maps demonstrating the inland routes by which these diseases travel. Diseases attached to specific regions of the world had their data merged and the cases mapped.  Two maps demonstrate how two African diseases followed two different routes into the US.   Sickle Cell followed the traditional slave population route, the other (infibulation, shown above) follows a more modern 20th century migration route.

Sickle Cell is biologically linked and genetic.  Infibulation is behaviorally and culturally linked, but only to specific parts of African culture.

Sickle Cell Carriers distribution

We can also simple map all the diseases that come in from a region and see how they are spread and/or diagnosed in the US.

African migration 2

This was done for Central and South America, Africa, Russia, the Middle East, Japan, and Australia, to name a few regions. Separate lists and separate pages devoted to these are also posted.  (Many of these videos appear in my two Youtubes devoted to this–an older Youtube page, which Youtube stopped me from publishing in, and my more recent Youtube.)



And Japan

A number of these have a Version 1 and Version 2.  Version 1 represents the obvious zoonotic diseases (i.e Venezuelan Tick Fever or Encephalitis). Version 2 represents additional diseases specific to that region or country ecologically (Bancroft Filaria), or culturally (i.e. Kuru), due to the nature of the organisms they are linked to.

United State “Regionalism” and Disease Ecology

In the United States, organismal and some microbial or bacterial diseases have specific distributions.  The following example of a disease very specific to Chicago climate and soil illustrates this spatial relationship.

Known colloquially as “Chicago Illness”, this video was developed to demonstrate the different methods of evaluating disease mapping outcomes.  I also use it to demonstrate the value of a method commonly used in remote sensing (satellite imagery analysis), in which N values per cell are squared to cubed to identify ecological disease centroids and “hot spots.”

As discussed in my thesis in 2000, and as introduced by Economic and Medical Geography Gordon Pyle, disease travel can be hierarchical or non-hierarchical.  This was covered as well in the following second version of the Chicago Illness video:

Another environmental disease group I identified for review of Disease Ecology pertained to specific geochemicals, such as metals, and the products of nature that influence our health and foodways.

Natural poisoning diseases (venoms and the like), related to place, occupation and landuse features (mining, domestic settings, occupation, or travel and/or recreational exposures) were assessed

Natural resource industries also produce regional health concerns.  The coal miners, woodworkers working with specific trees, and even farmers expose them selves to plant substances that many chemically, structurally and immunologically cause new diseases to develop.

Notice how the Mushroom Growers Lung is distributed (focus is on midwest, the center of one of the largest Armillariella organisms; is this for occupational or ecological reasons?).  This condition is generated by spore inhalation resulting in fibrosis, scarring, a pneumoconiosis like effect, and a reduction in lung elasticity and expandibility,

Chiclero’s Ulcer is a unique fungal infection of the ear found in workers of highly humid and moist tropical rain forest settings, namely the Yucatan Peninsula.  The distribution of this diagnosis in the US demonstrates the migration routes taken by these former outdoor workers from Mexico.


Again, the application of Medical GIS and disease mapping are numerous for the Managed Care industry.  With the Managed Care philosophy in mind, we can use it to improve our surveillance and design more effectively targeted programs for intervention or prevention purposes. have been defined, to prepare a program for changing rates in specific healthcare practices, disease rates and moral or ethical concerns.

The current failure in this system is once again in the delay of implementation of spatial modeling that is occuring.  The level of spatial modeling required by managed care to be more effective, and innovative, is to use it to monitor all health related facts, practices and costs.  The traditional GIS seems too cumbersome to use for this form of monitoring, which is why I am demonstrating only examples of the automated surveillance system spatial analytics system I currently use in large data warehouses and systems.

The next review of this Medical GIS will focus on sociocultural and human behavioral GIS monitoring practices and examples.



[reposted from LinkedIn]

It was only a matter of time before the findings and warnings I posted about female genital cutting (FGC) or manipulation surfaced in the form of actual case evidence via the news [links to these are at the end of this article].

FGC is a non-essential health practice that has been documented in the United States medical literature for more than 200 years. The first article documenting this practice in the U.S. is found in the Medical Repository, a medical journal linked to the first U.S. medical school(s) in New York, in which two cases involving late 18th century slaves were reported (see this article is at A disease peculiar to the children of negro slaves (1810).

The more recent attempts to publicize this practice with hopes for change, involved supermodel Waris Dirie, and Jaha Dukereh, who was highly active with the UN in 2015.

FGC or FGM is an example of a “culturally-bound” medical practice, and has its parallels with other such practices performed by other cultures. (For example, see my “FGM: Is it already here?” of June 6, 2016). The problem is, the long term effects of FGC are not considered when this practice is engaged in. In an interagency statement, the World Health Organization and others have asked that this practice be ceased [the resolution]. In a very recent conference devoted to this topic (March 13-14, 2017, Geneva, Switzerland), important updates were shared, including a report on the New York area (“Women Speak Out: Female Genital Cutting”, by Camille Clare, MD, et al., NY College of Medicine, Valhalla, NY).

In my national study of FGM (performed about 7-10 years ago, and charted for a population 0-85 yo), several age groups (age +/- 2 yrs) demonstrated greater amounts of reporting of this phenomenon in the EMR, for a 6+ year time frame evaluated for population consisting of MCR, MCD and commercially insured patients.

The following depicts the annual reporting rates for the most recent six years, as described by these data (left depicts rates. right depicts n; each n graphed as an age pyramid is preceded by total population N adjusted incidence/prevalence graph; using rolling 3 year averages, with number for age range 0-85 (cases>85 yo were excluded), graphed as rolling avgs, for ages 1 – 84):

My recent study of a large urban setting confirmed these findings, and demonstrated the possibility of an internal US practice being performed of this procedure, due to the number of children between 0 and 2 years of age found in both of my large population datasets evaluated for this ICD group. Both zip code and grid cell clustering techniques were applied to this work, using the 3D mapping technique I developed just prior to my studies.

The recent news articles on this topic pertaining to Michigan confirm my findings pn the “hot spots” for this practice in this country, according to the national EMR information that I reviewed, i.e. (but also see 3D video links that follow in the text].

This use of 3D mapping demonstrates the value of spatial analysis and the need to apply it, for a more thorough evaluation of its use in evaluating national EMR data.

The first maps developed using this non-GIS mapping methodology demonstrated how fast it can be used to produce outcomes, which were focused on presumably “the rarest of reported ICDs”. I used this technique to map out the distribution of ICD9-identified Sickle Cell and Sickle Cell carriers, and the other African American culture–ICD9-identified female genital cutting cases. This algorithm was then developed one step further, and used to produce videos of these results (see Sickle Cell (my see personal blog page on this), and FGM (the prototype and modified version).

This technique may also be used to identify hot spots for “immunization refusals” in this country (posted 6-12 months before the 2013 and 2014 small pox outbreak, and used to predict the recent spread of this disease due to immunization refusal behavior). [Review this SEARCH for those links]. It has also been applied to the study of Ebola behavior, using a method applicable to researching other international, zoonotic disease patterns [for which see].

Many of the “outbreaks” striking the news, now or in the future, are understandable and even predictable by requiring and then integrating EMR/HIT protocols with GIS and Spatial Analysis protocols within all managed care systems, at both the facility and business levels.

For an example of such a system devoted to health conditions as cultural phenomena, begin with my “Socioculturalism and Health” page. This anthropological interpretation of culturally related health care conditions, however, represents just one view of how culture affects the human health state. In effect, it is proposed that we analyze ‘culture’ and health as a four part model: i) review socially, culturally, geographically linked ecological conditions such as culturally-related infectious and human ecological diseases, ii) review socioculturally-linked human ecological/behavioral diseases, iii) review socioculturally- (or culturally-) linked ecological/biological diseases (which the present genomic and epigenomic study of disease is now eluding to), and iv) review the classic culturally-bound disease patterns. Several years ago this method was discussed extensively on several pages, beginning with “Applying culture to managed care metrics.”

The following links pertain to the present FGC or FGM events, and were reviewed for this posting (links are provided in temporal order, and were active on 4/30/2017]. They are followed by several more links to helpful readings.

NewsOne Staff. NewsOne, ca. 2012. Former Supermodel Fights Against Female Genital Mutilation. [Link] (about Waris Dirie’s case, which is perhaps the first publicly reported; for related Chrome search see Link)

Lucy Westcott. Interpress Service, June 26, 2013. “Q&A: How one Woman Demands Answers and an End to FGM.” [Link]

Barbara Herman. International Business Times (CDC), 2/5/15. Female Genital Mutilation in US: 513,000 Women and Girls Cut, CDC to Report. [Link]

Jaha Dukureh. U.N., February 6, 2015. “Feature: ‘I am not Whole’ — female genital mutilation survivor speaks out.” [Link]

Lucy Westcott. Newsweek, 2/16/2015. Female Genital Mutilation on the Rise in the U.S. [Link]

Jacey Fortin. NYT. April 13. 2017. “A Michigan Doctor is Accused of Genital Cutting of 2 Girls” [Link]

Lucy Westcott. Newsweek, 3/17/15. “How to end Female Genital Mutilation in Egypt.” [Link]

Merrit Kennedy. NPR, April 14, 2017. “Michigan Doctor Charged with Performing Female Genital Mutilation on Girls.” [Link]

Lucy Westcott. Newsweek, 4/18/17. “‘Religious’ claim by doctor accused of Female Genital Mutilation.” [Link]

Violet Ikonomova. Metrotimes, Detroit. 4/22/2017. Muslim Sect known for female genital mutilation responds to charges against local docs. [Link]

Mayra Cuevas. CNN, April 24, 2017. “Michigan Doctors charged in first federal genital mutilation case in US.” [Link]

Christina Cauterucci., April 24, 2017. “A Michigan Case Triggers Debate Over the Terminology for Female Genital Mutilation”. [Link]

Tasneem Raja, Ari Shapiro (HOST). April 24, 2017. “Writer Recalls Undergoing Female Genital Mutilation in the U.S.” Heard on ‘All Things Considered.’ [Link]

Tresa Baldas, USA Today/Detroit Free Press. April 27, 2017. “Report: Girls’ genital mutilation injury worse than doctor claims. [Link]

Tresa Baldas. Detroit Free Press. April 27, 2017. “Senate Moves to ban genital cutting in Michigan in wake of Detroit Case.” [Link]

FGC in Finland. (A Public Health report)

Eliminating Female Genital Mutilation. An Interagency Statement. OHCHR, UNAIDS, UNDP, UNECA, UNESCO, UNFPA, UNHCR, UNICEF, UNIFEM, WHO.

WHO Resolution on FGM. Main Page. “G. Female Genital Mutilation (resolution WHA61.16)”, pages 12 – 14. (Specific Resolution accessed at ) For more, see FGM Switzerland Conference Search.

Female Genital Mutilation/Cutting. Management and Prevention. Sharing data and experiences, improving collaboration. March 13 & 14, 2017. Geneva University Hospitals. 8 page schedule. Accessed on line. [Abstracts not yet published(?)]

Google Search for Books on this topic.


Applying my method for modeling care to cases, we can begin to develop a method for evaluating the complete quality of care a person receives throughout his or her lifespan.

This method is made simpler by evaluating all of the processes of care by relying upon  methods that break down the care in different parts –defined by the meaning and purpose of the care–such as regular visits, versus emergent care, versus hospital-based need, and/or the need for special surgical needs (to name a few).  Some databases are designed to facilitate that method of subclassifying all of the care services.

By looking at the care processes using these groupings, and then assessing the care by the age of the victim and determining if the care is preventive, palliative, remedial, or reactionary, we can learn a lot about the health care process.  Are certain institutions or care givers more likely to work at the latter end of the listing just provided?  Are they primarily preventive healthcare givers, or reactionary and palliative or remedial in nature?  By applying this to how perform their services for each health condition, we can determine how effective a healthcare system, institution, facility, program, or practitioner  is when it comes to meeting the needs of their patients who are becoming older, evolving new risks over time, predictable at times due to the nature of health in comparable patients enrolled in the program(s).


These next few case examples are of these theoretical patients.  They are receiving the same baseline care from their primary care practitioners (PCP)s, but are experiencing different health conditions and medical experiences within the care system due to their differences.

It is assumed for the moment the each patient’s manner of receiving and affording healthcare doesn’t change throughout his/her life.  So the transition from medicaid to medicare at 65 years of age, or the changing of insurance coverage once a kid turns 18 (plus or minus with Obamacare), are not applied to this model.  Likewise, a patient’s change in coverage expected for changes in career status are not, for the moment, considered in this method for modeling care.  (But believe me, the inclusion of these changes in life will be added to this model over the next few months.)

So, only the major health or disease problems are looked at.  Each of these, as described elsewhere, consist of processes that are provided by the physician as a standard part of the healthcare process, and are not included in the billing of care.  Other parts of the care process are billed, like the labs, use of most of the diagnostic testing equipment (xrays, MRIs, etc), engagement in the use of certain medical equipment,  the cost for medications, etc.).


Example of assigning costs for theoretical cost analyses.  These are the procedures engaged in as part of a standard PCP visit, for preventive care, in the form of an annual visit, followed up by two lab-related visits and a renewal of medications. 

Therefore, one part of this evaluation method being developed  enables an analyst to compare care processes to which costs are assigned and billed, against those which lack cost and are provided for “free”–such as giving a patient educational materials, making recommendations about diet, taking the vital signs of the patient, providing a reference to another clinic–none of these should be charged to the patient, and are generally considered part of the standard primary care process for the patients presence or stay within the facility (during a doctor visit or while lying in the hospital bed).

The special procedures, however, do have costs attached to them.  Evaluating this care model I developed, we can look at cost-related procedures versus unbilled activities, to see how “rich” the entire care process is, with regard to the processes the clinicians opt to engage in (or try to save money by not including in their care process).  This method may also be used to detect MDs that favor certain processes over others, due to procedure type and name, and/or cost (i.e. avoiding the less expensive PET scan in exchange for ordering an MRI or CABG process).

The first example above is of the HIV case I presented a month or two ago.  Instead of renal complications, this patient experiences endocarditis onset in his/her forties due to the use of unsterilized needles.  These two scenarios have different onsets of complications at different ages, involving different organ systems.  One research question to ask regarding this scenario is:

  • Which of the two possible experiences will cost the patient more?
  • Assuming that otherwise the two health care experiences are the same, where will the costs differ?
  • Which one of the two may have post-procedural processes that cost the patient more?


Note:  For the above scenario, the case is historical, but the theoretical application is modern–the heart surgery was of course not available for Dr. John Kennedy Bristow at the time.  For more on his life, see the pages I posted on him elsewhere on this website.

This next scenario is of a nineteenth century physician I biographed about 25 years ago.  His entire life’s medical history could be evaluated due to the records he kept, which are in possession of the Oregon Historical Society.  He experienced rheumatic fever as a child, and this fever ultimately had long term effects upon his health as he aged.  His changes in occupation, and his notes as a physician about his health, enabled me to produce the above simplified time chart of his life health experience.  This will be evaluated on a future page.  For now, this is meant to serve as another example of graphing or depicting a patient’s lifelong health experience.  (He died at 83; for this presentation he will be reviewed at the age of 57.)

These next two cases are of a patient who unfortunately suffered a number of pulmonary related complications in life.  The second patient is facing the consequences of a high BMI at a very young life.

The figures demonstrate the difference experiences the two cases had over the years.  Each one of the ovals is a visit (or set of visits and procedures), with a cost attached to it.  Each branch in the healthcare process pertains to a different ongoing health issue.  Some of these issues engaged only clinicians.  Others engaged the mental health staff.  Still others involved the special services made available to patients who experience some sort of handicapping effect of their conditions.

The pulmonary case is a reflection of several types of individuals I worked with during my stay in the Pacific Northwest during its peak Old Growth Forest years.  The pulmonary condition defined by the local geography was a form of Occupational Lung Disease, in the form of Hypersensitivity-induced Alveolitis, due to the sequiterpene lactone (SQL) rich liverworts growing on old growth forest trees.  Tree cutters who cut the trees along its trunk and base were exposed to dust generated by this plant, which often covered the tree bark of the oldest trees.  By inhaling it, they exposed their upper and lower respiratory passage to the powder, and the SQLs released by it.  The result was a combined obstructive-, chemically-induced autoimmune form of pulmonary disease, that resulted in the swelling of the surfaces of bronchioles and alveoli. (for more, see my page on this.)


For a young child with asthma conditions experienced at school, for example as a result of exercise, growing up in this area may expose the child unnecessarily to this environmental risk factor capable of causing severe chronic obstructive pulmonary disease problems during the later life.  Young workers, unaware of these potential long term consequences, increase their risk further by obtaining fulltime work in this exact industry (due to the rareness of other occupations for certain regions), and/or by engaging in either tobacco or cannabis smoking activities, thereby further exposing their lungs to potentially irritating inhalants.  The above model depicts what we’d expect a typical Pacific NW lumberjack, with a history of asthma or COPD at midage due to lifelong smoking, to experience were he to be employed in tree felling, or even in the cutting of raw wood into lumber, with or without adequate respiratory gear.


The next case, a child with morbid obesity developed at a very young age, has several factors to be concerned with, which in turn effect how that child’s care may be evaluated and monitored.  First of all, there may be a genetic reason for this problem that needs to be evaluated.  Second, family history, family members, and family-promoted lifestyle may be a factor in defining how and why the child became morbidly obese during the elementary school years.  Third, social and community related behaviors generated in response to his or her appearances and medical status may also be partially at fault for this state, determinable by engaging the child in a psychological assessment, and perhaps later, assistance by a nutrition specialist.

The final factors ultimately determining the child’s potential lifespan depending upon the other consequences of this obesity, defined by what is currently termed “metabolic syndrome” related obesity and the subsequent engagement of other organs and organ systems, ranging from the vascular system due to the onset of hyperlipidemia and hypertension, to the onset of heart disease, gall bladder disease, pancreatic disease (including pancreatitis and obesity-induced, formerly “adult-onset” diabetes).  These in turn may also be linked to renal failure, loss of vision, and peripheral neuropathy, loss of circulation, and loss of distal appendages due to gangrene.

Both of the above conditions demonstrate how problems experienced as children during the elementary school can have serious consequences on lifespan and quality of life during the later decades.  Each of these patients experienced a unique pathway that is in fact fairly common to many patients in the U.S.

These last two cases, for this review, are chronic conditions with lifelong implications.  Both are of real people.

The first is a female with retinitis pigmentosa identified during her mid-teen years.  This genetically-based disease is the first of its kind diagnosed for her family.  During her later teen and potential-college years, it impacted her decision making regarding career choice.  She went to college and earned a bachelors degree followed by a terminal degree in the allied health sciences.   She then toured the world, learned a variety of complementary health philosophies and practices, spent three years at a retreat, and then returned blind to commence her work in healthcare.  She experienced few other problems due to her health or physical state, but required considerable assistance at the social services and physical/occupational therapy levels.  As a result of aging, physiological changes impacted her health slightly.


The second is a person who experienced some health conditions that possibly led to the onset of seizures during the preteen middle school years and the diagnosis of epilepsy by 7th grade. This impacted employability and career choice, leading the person to attend college for a bachelors and terminal degree.  Epilepsy related problems began at midlife, during the immediate pre-career years, leading to the need for a variety of medical and  allied health services, and social services.  Ultimately, the problem was alleviated, enabling the person to return to the workforce and experience health problems common to aging in those with a separate family history of older age disease.

The exercise in understanding this method of organizing an analysis of a patients total lifetime, healthcare experience, is to consider the numbers and costs for specific groups of events or experiences, for each of the primary health problems noted by the titles at the top of each set of healthcare operations or events.

These are simplified examples.  They exclude old old age related events and the impacts of aging once the age of 65 is reached and the patient becomes eligible for Medicare coverage, and perhaps special housing (long term care, assisted living) where healthcare services are available as needed.


Each visit has a cost attached to it, as does each billable procedure, each operation, and each billable action that is needed to engage in a diagnosis or improvement in quality of life/lifespan for a patient.  Theoretical costs can be assigned to each step and each branch in this model.  More branches can then be added to evaluate the minor differences in health care, such as those who undergo organ replacement, the initiation of dialysis care, or any other unique process not commonly considered when total cost is the concern for prediction modeling.

More of these examples of ICDs and their costs are currently being developed.  This model may be used to evaluate any healthcare processes or programs engaged in long term care management.  It may also be used to serve in prediction modeling, of cost, or expected outcomes, for given patient experiences before and during the healthcare process.



[See prior postings in this blog for a detailed review of the above chart.]

This therapeutic model focuses on the theory that the basic pathway for consists of basic elements or parts that define the entire care giving process.  This model starts with the PVP or PVEP assumption, which states that a single patient comes in for a “visit”, which consists of actions or events, and procedures, either of which are engaged in by the provider, his or her medical assistants or staff, various lab technicians, adjunct therapists, counselors, etc.

These series of actions or events that occur all take during one attempt to obtain care.  This attempt to obtain care may be as simple as the basic walk-in or annual care visit, or a scheduled visit for an x-ray, EKG or EEG, or a visit to see a specialist, an emergency visit, or in-patient activities that last for days to weeks.  The “visit” ends when the care process is completed, and the patient is “discharged” according to electronic medical records.

From admission to discharge constitutes a single care visit, and many billing programs now charge the patient for the visit itself, and for particular portions of that visit considered billable.  In more advanced programs, the entire care process or “episode” is evaluated for the billing, known as “bundled”, meaning that an episode that requires only basic care related processes is usually charged the same as an episode for identical care that had complications and required twice as many days, visits, actions, procedures and events.

Many programs still base their billing on a combination of specific billable procedures combined with basic “bundled billing” or some sort of multi-tier (multi-level, multi-complex) care giving process, whereby the basic bundled billing events are still charged equally in spite of their greater frequency in some cases, but these amounts get adjusted to low risk (average or low days of inpatient stay), moderate risk (more than average days), and high risk (many days) billable status.


[For a detailed review of the content of each of these ellipses representing a visit, see the prior posting on this blog.]

In this basic model there are 8 regular PCP visits, to which 6 special visits are added for care by Specialist 1 or “Spec1”.  Specialist 1 related activities in turn result in some discoveries about the patients health status that require a follow up by yet a more specialized care giver, “Spec2”.  Then, during the evaluation and care process initiated by Spec2, a new problem emerges, requiring intervention by Spec3.  Throughout this time, the patient keeps up his/her visits with the regular PCP.

This above model could very well fit for a midlife person with a diabetes and weight history in need of ongoing management, with endocrine related complications  resulting in the need for Spec1, followed by onset of a cardiac problems requiring a heart disease interventionist. Due to the onset of the heart disease condition, the patient develops a need for even more specialized cardiac care, such as a special diet therapy and related surgical intervention (i.e. “stomach stapling”).


Note, this model does not focus on just regular clinical visits.  It incorporates emergent care and inpatient visits/events/activities, to fully assess the therapeutic process that is engaged in.  In theory, one can compare services and outcomes between agencies, providers, companies, patient groups (race, ethnicity, age, gender, etc.).  We can even add some basic cost-related assumptions to this model, and estimate the costs of each of these visits, at the different specialty levels.  A common pricing theme used in the past for this modeling has been

  • PCP level (“CP”) = basic PCP visit cost + lab and imagery costs
  • Spec1 level (CP, but in a specialist’s office) = 1.4 to 1.6 x PCP visit cost + lab + imagery costs + additional therapeutics (genomic testing, nutrition, etc.)
  • Spec2 level (ditto) = 1.6-2.0 x PCP visit cost + lab + imagery + additional therapeutics + chronic care related events/procedures costs
  • Spec3 level = 2-3x PCP costs + lab + imagery + unique single event- or care process-linked high cost end stage and/or surgical procedures

It helps to add true prices to these events, but theoretical pricing is also very helpful.  In addition, so long as the complexity and true cost relationship of later tests are maintained as tests, imagery, therapeutics actions are added to each level, a fairly useful comparison of costs over time can be made for this simple multiple acute/chronic disease patient.


The critical costs developed by this patient take place in the end stage of his or her state.  Examples of these events are those that lead to kidney failure and the need for future renal care, acute heart failure brought on by congestive heart disease, heart valve prolapse and failure related to childhood rheumatic fever history, atrial fib or flutter initiation due to underlying genetic, stress and metabolic patterns.

In the above example, this could be interpreted as a patient making a regular visit with a specialist, followed by an unexpected emergency care event, in which the specialist engages in the necessary hospital visits, followed by the decision that some sort of surgical process is needed to improve the patient’s quality of life and perhaps increase his or her lifespan.  This particular set of events can be likened to the need for a surgically implanted defib-arrhythmia device, or the need for a oncology surgeon due to a newly discovered metastasis.


A summarization of this type of process, using the basic modeling technique I developed, is depicted in Figure 4.

The processes that take place for each visit, inferred by the first figure, and which are fairly complex are described in the prior two postings on this method for evaluating managed care that I developed for EMR and Big Data evaluations.

This method of modeling, evaluating and illustrating the results of a study on healthcare patterns can be used to demonstrate differences in practitioner or system therapeutics behavior between various groups.  These groups may be:

  • mental health [MH] vs. primary physical health {PH] care groups (historically, MH is reduced and PH is increased)
  • ED and Ambulatory Care (walk in therapeutic or testing procedures) activities in white vs black populations, or hispanic vs non-hispanic patients.
  • IP lengths of stay and activities engaged in during those stays for one group versus another, or one facility versus another
  • The amount of reliance upon prescribed medications vs non-Rx methods for prevention and treatment (i.e. exercise nutrition, counseling) for PCP care versus specialty care treatment patterns.
  • The ratio of educational, health focused discussions and materials provided to patients, versus cost related imagery and lab events, in program 1 versus program 2.
  • The amount of engagement in genomic testing for ethnic group 1 vs. ethnic group 2

In sum, the simplification of the therapeutic process that this modeling method offers researchers has applications across the board for EMR review and quality of care and service evaluations.  It may be used to target just specific activities, such as meaningful use measures, or be used to evaluate the entire care process for a complex series of diseases, or applied to analyses and comparison of office visit or hospitalized patient care events (i.e.–in which patients do immediate follow-up proceed more quickly, leading to discharge?).

When costs are added to this model, it can be used to evaluate where excessive costs exist due to systems-related healthcare or administrative defined (policy/procedure related) quality of care or chronic disease mismanagement.

Figure 5.  Example of a Case Study review for the evaluation of CP, EP, IP and RP therapeutic processes (RP is inferred by arrows or lines leading to new treatment pathway).  Case is a male patient with 45+ years of HIV-Drug Abuse history and related secondary diagnoses, including a 10 years delay in enrollment into an abuse therapeutic program, two periods of mental health assessment and/or treatment, and pre-renal failure at 49 years of age leading to possible onset of Renal Failure by 57 years of age.





When we look in detail at the process a person has to go through to experience a disease, or a diagnosis in need of a treatment, some methods of treating the illness make sense.  But most of the methods employed to treat someone who is sick as designed lead the patient along one of various pathways to “the cure.”

The purpose and value of health care evaluations using statistical measures pertains mostly to the latter.  The pathway that needs to be taken by a healthcare may not necessarily travel a route to “cure”.  This is because we don’t actually know the process required to instigate the “cure”.  We only know the best path to take to approach the “cure” of a disease.

The annual HEDIS review of healthcare systems is designed to evaluate the quality of care [QOC] by an organization or program, with the goal of improving the outcomes of care that an institution provides.  As part of the annual HEDIS review, there are certain protocols that most or all Medicaid, Medicare and CHP programs have to engage in, in order to qualify for governmentally-approved coverage of these patients, in order for these agencies to pass the annual HEDIS inspect and have their contracts renewed.

More recent attempts by the Obamacare or PPACA program, now about to expire, mimicked some parts of these ambitious QOC minded goals, that I have been engaged in for nearly 15 years.  In the beginning I strongly supported PPACA because it required targeted analysis of goals and accomplishments, and served to increase the development and use of electronic medical records (EMR) systems.  In the long run, I felt such a goal made whatever analyses were engaged in for QOC more revealing of its failures and succcesses, providing strong evidence for why some of these programs should fail, whilst others may mess up and barely make it (ultimately failing the one year probationary status they are hand).  This means the best insurance programs should ultimately outlast the rest, by remaining financially successful and highly accomplished health-services-wise.

PPACA was supposed to ultimately take scoring QOC the “make or break deciding factor” for a program, relying upon the demonstration of a program’s actual health care skills, not simply billing, reimbursement and complete payment successes.  These skills focused on the individual and team work strategies that health care services work by in the care of patients.  Ideally, it implies that an excellent health care system can effectively treat a patient and then discharge that patient at the right time . . . regarding the ability of that company to fully and appropriately treat a particular illness that the patient is being hospitalized or treated for.

In fact, this is also why one standard measure for nearly all institutions analyzing their care process is to measure the rate of return of patients to the hospital setting (first defined by Yale in the late 60s, early 70s I believe).  This metric evaluated how many patients  were readmitted for the same condition, usually within 30 days–a process known as readmissions.  [The other standard measure for the time was number of open beds, per month.]

These studies taught us that patients who go into hospitals don’t leave because they were cured of their problem, usually.  They are discharged because whatever health care actions are still needed on their behalf can either be performed by the patient himself or herself, or with the assistance of a family member and/or home nursing care assistant. (Some people claim it’s “to save money”, which is only true with regard to requirements for re-approvals generated by insurance companies.)

What PPACA started — the evaluation of outcomes of care as a means to measure success —  took a turn for the worse when EMR value assessment became a part of the requirement for the healthcare company to be fully enrolled and engaged in PPACA or Obamacare, and insurance agencies were unwilling to comply.  For hospitals, cost was only a factor regarding the type of software and hardware requirements that were needed to comply; the upgrade of IT systems and hiring new IT experts were a barrier as well, but only a temporary barrier.  (Evidence of compliance within a health care establishment is quite visible; they utilize Health IT equipment when a patient arrives for care. Insurance companies, on the other hand, require internal systems to fully evaluate care processes engaged in clinically, in addition to and distinct from the regular review they have always engaged in regarding claims, cost and billing, using a completely separate — often by law — healthcare IT system.)

So, basically, the idea of being able to simply gathering your data into a database, and then reporting these data in a useful way seems simple.  It is a primary requirement imposed on companies by the managed care systems in general, and in response to the HITECH Act passed by the George Bush [Jr.] Administration.  In theory, any company could (and can) do it, given it has software and hardware that are required. But, for some reason, many companies could not do this simple IT task, leading CIOs, Directors and Managers to sometimes “fudge” on their data requirements, in terms of gathering, submission and reporting.  [The last “meaningful use” fraud-ridden report was about two years ago, bu a company down in Texas, if I recall correctly.]  These agencies, companies, facilities responsible for fraudulent reporting seem to forget, the primary purpose for PPACA is/was also to prevent fraud, with regard to reporting patient data, in particular, those double billing events and/or misbilling practices commonly cited for “care event not fully completed”.



The second and equally responsible reason for PPACA failures during the past 12 to 24 months is most certainly the failure of insurance companies to appropriately manage their data, and use it to develop a comprehensive population health surveillance and monitoring system.  This is quite a generic claim for me to make, but my evidence for it is quite simple.

Companies remain focused almost completely upon PPACA requirements, such as reporting the required “meaningful use” metrics (usually just 40-80 per year).  Meaningful use metrics, although important and designed to target large scale care practices and issues, demonstrate “favoritism” towards just a few diseases that people experience.  Rare conditions are ignored through this process.  Rare conditions that serve as high cost cases requiring lifelong high cost treatment and ongoing care related events, are for the most part ignored by insurance companies that devote most of the quality of care efforts to just meeting these few requirements.

This the reason for this venture of mine–to develop a workable model for evaluating manage care performance over a patient’s life time, per provider, facility and assocaited health care system or insurance program.  This model I developed produces highly detailed analyses for patients at all of the AGRER (Age-Gender-Race-Ethnicity-Gender) and SES/spatial levels required for my level of statistical epidemiological surveillance and reporting work.  In theory, the knowledge for how this is done can be developed by other teams, if they have the necessary human resources and highly skilled analysts and programmers needed to perform such a rare act for the typical health insurance (national-level) company. (I realize, they are same name, different COs and Directors).

National health insurance companies refuse to employ the needed surveillance processes, and comply almost entirely with just PPACA Meaningful use like reporting requirements, because they have not fixed the management, employed new innovative minds in order to produce an effective population wide, spatial surveillance system, for all kinds of health risk groups, not just the Meaningful use groups, not just the HEDIS defined groups.


So that is my complaint about the insurance system and why INSURANCE COMPANIES are primarily why the recent HITECH derived goals in managing health care area not being reached by the major payers for these services.  Those which did not sign onto PPACA were either afraid they could not reach this goal requiring improvements in their processes and/or technology, or were truly incapable of not reaching such goals.  Like laggers in a herd of buffalos, they pulled this healthcare system back towards the remains of much older members. The patient’s “best health” was not in mind; it was up to them to provide “the best health service” for the money.

Nearly everything done in medicine is “practiced” with the goal of “curing” in mind, but in reality is perceived, performed and measured as part of a route taken towards this ultimate destiny.  The goal of many programs, as defined by insurance companies management, is to provide the best quality of service for the moment.  If that means your surgery is delayed because a part of you “isn’t gangrene yet” (i.e. abdominal or hiatal hernia), their system deemed that decision the best in the patient’s interest, in combination with the best in the company’s interest.  [This is an argument we have heard many times before.]

In health care, the way the health care system operates, the way that the process of caring and “curing” is carried out, there are really just a few kinds of activities that happen to every patient, relative to how he or she spends his time, makes the trips that are necessary, and either accepts or denies those processes by which “medicine” can be “performed” and “practiced”.

That is what this part of my review covers regarding how to score the success of a health care program.  Ultimately, it related to how much health will cost the system for the same illness with different consequences or different methods of treatment.


The above is an example of lifelong epilepsy care.  I’ve posted this model several times at different sites.  It is based upon an evaluation of costs accrued for treating a lifelong condition, derived from true time, true healthcare procedure and cost derived data. [LINK to this earlier posting on this site, and my other site devoted just to this topic.]

An effective allopathic health care system has two parts to the initial care process, and a third for highly advanced cases.  There is first the regular doctor in Internal Medicine or Family Practice, and/or the generalized care giver who is a specialist, like a pediatrician, a gynecologist, a geriatrics expert.  Second, there is the specialist who treats you for your special chronic or aging disease problem–usually  specialized in one organ or organ system–the cardiologist, neurologist, nephrologist, oncologist, etc.

The third part of allopathy is that specialist devoted to a life threatening or life-shortening malady, the quality of life experts who are involved with skilled nursing, improving quality of life, and improved end-of-life healthcare strategies.

Then there are those non-allopathic “healers” we all learned to rely upon, ranging from family member(s) to licensed specialists, like the osteopath, naturopath, chiropractor, traditional chinese medicine practitioner, acupuncturist, etc.  There are also the LPNs, and midwives serving patients where their practice is allowed.

This definition we provide for “specialist” is a cultural one.  In the U.S., and in a non-traditional sense, a specialist can also be a shaman, curandera, midwife, yerbero, Christian scientist, or laying of hands/intercessory prayer practitioner. For each and every patient using nurses, doctors, specialized the doctor ultimately becomes the person we want him or her to be for us.  Those methods of practice which we believe in, and allow to be “practiced” upon our body (or mind-spirit-soul connections) , play a most important role in whether or not medicine will “work” on [in] our body or not.

The current process for health care can be evaluated and measured by looking at how many ways we try to be cured, how many actions we engage in with this hope, who and what kind of healer we are engaging in these processes with, and how many steps does it take to go through these processes, what exactly are these processes and how and why we allowed them or selected them.

In the health care information technology system, analyzing a person’s healthcare receiving process involves analyses of the person or patient, the illness, the visit to the doctor for this condition (how often, to receive what form of care), what events the patient goes through  alongside that practitioner, and what formal medical procedures are attempted as part of the health care “practice.”


The steps one goes through when being “treated” seem unlimited at times.  In a simplistic review I recently did of 95,000 types of procedures that doctors get engaged in, I was able to determine that there are essentially 20,000 to 25,000 of these procedures that are most important and appear for many people.  The remaining procedures are additional or adjunct processes that are engaged in for fewer people, when the basics do not work.

These procedures don’t include the typical human-to-human interaction related behaviors, such as educating a patient, giving the patient something to keep a record with, providing an explanation for what needs to be done next and then forwarding that patient to the next specialist in charge of labs or x-rays.  These “actions”, that don’t involve any scientific measurements used to evaluate health based upon numbers, constitute ideas and activities that are shared and often happen with the doctor, nurse, technician, counselor, and patient.

In the simple flow chart illustration drawn up for this procedure, above, some basic examples of these types of steps are provided, under the Laboratory and Imagery lists.  The visit activities and events detailed briefly are each and every procedure or thing a doctor does while he/she is serving you.  In a good health care information technology system, every behavior a doctor becomes engaged in can be monitored.  These data are kept in the registry of the visit for that day, for use by many health care providers, then billing agents, then insurance companies, to see what has been done.  In the end, little of these processes are paid much attention to, except with regard to how the care process might be billed.  This action may be taken to develop a detailed accurate history of the health care process, and a detailed listing of what was done, to determine which of these are approved for the billings processes.

Until recently, mostly insurance and billing agents have paid attention to these data–in their entirety.  Very few individuals interpret their logic.  For example, it is possible to determine if a set of actions taken are the best actions to be taken for that care process.  These types of measures are rarely delved into, within past HMO as well as current managed care programs.  But such a detailed review of the healthcare process can be done, to determine where during an office visit a delay cost the company twenty minutes in patient care time involving an examination room, or when during the hospital stay too much time elapsed after a test was ordered and the actual test performed.  It is possible to evaluate a series of healthcare related activities, by the minute, with the new data systems being developed.

The following figure is a model of the processes that are engaged in with a single visit, regarding a single set of healthcare operations.  It details the measurables or metrics that may be analyzed for each of these visits.


PVEP = Patient-Visit-Events_Processes or Procedures; QOS=Quality of Service; QOC=Quality of Care; PCP = Primary Care Practitioner; ICD = International Classification of Disease (referring to a disease code or identifier, or group thereof); F/U = Follow-up.

We can measure patient, physician, practitioner, and health staff related events, what they are and what they are intended to measure or influence, when they happen, with what steps and physical parts engaged in this process, and what outcomes are occur with such processes.

Example 1

A patient with a specific history, experiences a specific series of medical or health related occurrances, resulting in the need for the visit, and then care; and then during that visit, undergoes numerous processes by the healthcare staff to be fully and effectively “treated”.

Examples of “Events” that occur with each visit are as follows:

  • patient is greeted, visually identified to be present
  • patient fills out a form (if new)
  • patient undergoes health queries
  • patient fills out insurance/payment form
  • patient fills out personal identification related info, such as race, etc.
  • patient validates identity

This patient then sits down and waits for his/her name to be called.

  • The patient gets called into the clinic and is led to a room

The patient sits some more, then the nurse of PA comes in, followed by:

  • initial hello and reason for visit query
  • question regarding other health items that may warrant attention
  • question concerning mental health or general attitude
  • information about health related habits, like smoking, alcohol consumption, coffee or tea consumption
  • information on prescription drugs
  • information on over the counter drugs or nutritional supplements being taken
  • information on other health care practitioners that may be seen

Up until this moment, every event that has happened is a procedure, which if entered onto a healthcare history reporting screen for the patient, ends up receiving a time-stamp for the event.  Some events are saved in parts.  Like if the smoking, alcohol and coffee or tea consumption are answered, and then saved, followed by the two question based mental health query.  A “No” for the latter, indicating the patient is “not happy” would then require a follow up mental health query asking five to seven more questions, a data entry entered separately.

Next up are the vitals, of which Temp, Pulse, Respiratory Rate, and Blood Pressure are measured, each one entered separately as one event, each with its own procedure code or other form of identifier, in the form of text or number.

A few minutes pass.  The regular doctor returns, and follow up on the history and review the new observations made, in reference to any past historical information that exists in the patient’s electronic medical records.

The doctor leaves the clinic, and then returns with the orders ready to be stated or given.

  •  Labs are ordered
  • A diet change may be recommended, and an information pamphlet describing it provided
  • exercise may be recommended
  • consideration of a screening for a new blood test (HIV) or an update on the immunizations
  • recommendation for of a flu shot is given
  • a visit to a counselor or specialist regarding a special medical state may be recommended
  • consideration of a screening for cancer is recommended
  • prescription is suggested; which, if the patient agrees with, the MD can enter a request on line in front of the patient for initiating that therapeutic process

Once all of this is done, the doctor then clicks on a box indicating all entries are made, and that this part of the visit is over.

In the minutes, hours, days ahead, as the patient completes each of these tasks, those actions are also noted in the electronic medical records [EMR].  Time stamps are provided for each of these events.

While still in the clinic:

  • an EKG is then done by a medical assistant
  • B/P is retaken.
  • More educational material is provided.

The patient is suspected of having a heart problem, so now has to see a cardiologist as soon as possible.

As the patient leaves, passing the billing clerk, the final steps are taken to close the event for the day, and a new appointment is made, a process also time stamped in the EMR.

Follow-ups to each of the requests by the physician are expected to occur once the patient leaves.  If successful, the following may be entered into the EMR by other allied health people regarding these later data points:

  • outcomes for labs, including tests requiring patient commitment, such as early morning fasting blood glucose levels,
  • the results of the Xray visit and requested dyes process (when it began and ended)
  • the radiologist’s report on these images.
  • fill or refill of a medication

Also, those that are missed are not entered, and their non-engagement gets documented during the next visit.

No visit to the counselor is documented, by either a counselor or the patient during the next visit with the primary physician. If a second opinion was requested, once approved, that would be assessed as well by or during the next visit.  The use of a health monitoring/reporting program for quality of health assessments (daily B/P and pulse, length and time run per day, post-run pulse, etc.) may be managed by an internet server or wearable device.  Dietary notes and other records kept in the form of structured and/or non-structured data can be analyzed for a perspective on the patient’s self-directed “health consciousness”, between visits.

The next visit validates which of the prior recommendations the patient adhered to, adding its own notes of events that take place during this follow-up visit three months later.  It also reinitiates this entire review process from the beginning.  When these visits are reviewed in relation to each other, and in relation to other forms of care the patient is receiving, we are able to evaluate the quality of care that patient is receiving, and producing for himself/herself.

Most importantly, long term care processes for very sick people with multiple disease histories can be evaluated using these same processes.

Example 2


The above is a complex case, where the patient sees a regular physician, but then must see a specialist due to an unexpected event like atrial fibrillation detected by EKG.

As a result of this diagnosis, the patient ends up undergoing a new set of visits, most defined by the impending health failure problem.

Each of the ovals in the above figure represents the same subgroups of procedures that occur as a “single visit unit”.  Also noted in this model is the more expensive specialized, surgical care for a patient with an unhealthy atrial fibrillation-atrial flutter state, and the long term actions that are engaged in as continual care for this case.  These additional events serve to increase the lifespan of the patient, and to improve the quality of that life during the remaining end-of-life years.

These processes depicted in the above figure include the following care related processes:

  • continued care by the regular practitioner
  • continued care management for other borderline or actual medical problems
  • cardiology care visits
  • implementation of a temporary ventricular fibrillation apparatus
  • evaluation and preparation for possible permanent implant of a device to treat arrhythmia, with or without the defibrillation option
  • Completion and follow up care for each of these processes
  • Visits with a specialist in order to more fully manage both the chronic disease and cardiac care.
  • Visits with a nurse and/or social worker for reasons related to living place requirements and needs (can the patient live at home? of in a long term care facility? or skilled nursing facility?)

The evaluation of each of these processes, their timing, their outcomes, the fact that they happened, can be done using EMR.

For further background on where these data are coming from, most of the events related to a visit are in the form of office and care related procedures, kept track of by the EMR system based upon identification of the form type and content typed in by the practitioner, and the time stamps for these entries once the entries are completed and saved.

More on these Processes

A fully documented health care process involves about 0.5M possible “actions” that can be documented (but usually just a few for each visit), with 20,000-100,000 processes or “procedures” that can possibly be done.  These focus on the basic lab tests such as blood, urine, and other body fluids tests, immune system tests and results, biological or organism culture tests and their results, genetic screening outcomes, etc.  The second most commonly utilized diagnosis process, specific procedure and hardware wise, are the imagery processes and the like, such as X-ray, MRI, ultrasound, CAT and PET).  Unique energy-derived measures such as EEG, ulnar nerve activity, functional neurological PET scanning, and the like are also specific procedures that can be documented, counted, assigned values to regarding outcomes, and then statistically compared.

This means that a single visit might engage a doctor and nurse or staff member a few times, result in a few tests, with a few dozen or more results reported, followed by dozens more data forms related to follow-up notes and external care, such as allied health care related events, walk-in procedures, social service related processes, and other health procedures requiring additional approvals.

This means that one patient, one visit, can results in dozens to hundreds of events and procedures.  Most of my reviews of patients-visits-procedures depict ratios of about 3 to 7 same coded visits per year for patients on the average, with a 6 to 10 count for the most basic procedures engaged in per visit.  Depending on the procedure, it might have only 6 to 10 rows of data per procedure and event (i.e. a scan process from start to dye injection to finish),  or 40 to 60 rows of data (a 40-60 part comprehensive blood test).  In the most basic numbers, this means the patient has one visit with 6 to 60 procedure related processes for the one visit, plus whatever other procedures are added to this value based upon the other procedures performed.

In terms of billing, only some procedures are billed.  We don’t bill a patient for the time to find and hand him or her the educational materials for example.  Relating this to billing and billable times, only certain parts of these actions and procedures are attached to the billing claim. A tentative estimate of ratio of unbilled to billed events is 1:1 to 2:1, for the “average unhealthy person” [I will discuss this topic more at a later time, once this calculation is run on new data.]

This simple model details only basic activities engaged in during the healthcare patient-visit-event-procedure (PVEP) process.  A more realistic model would have up to twice as many visit related events, and 3 to 5 times as many procedures, with that value increasing exponentially with each chronic disease that the patient experiences.

The following depicts how costs may be attached to this process, for one theoretical patient-visit-events-procedures scenario.  The example is of an obese, prediabetic patient, with weight-induced sleep apnea, a possible history of allergies and respiratory complications, possible asthma.  The PFT is a Pulmonary Function Test performed to evaluate his/her inspiratory and expiratory flow patterns, for diagnostic purposes. The patient refuses the PFT and several preventive care program recommendations.

The costs are, of course, “theoretical”.


Final Thoughts:

This model is my invention, 2016, and is a product of analyzing ‘Big Data’ for 35B EMR PVEP rows, 11M patients, in a 50×80 mile area. 

  • VPR’=9.77
  • EPR’=187.89
  • EVR’=19.21
  • RVR’=86.59
  • RPR’=846.55
  • RER’=4.505

(P=patients, V=visits, E=events, R=rows, R’=ratio)











With the recent passage of an Act proposing the elimination of Obamacare, it seems time to define why those arguments we always hear about controlling the cost for health care can seem ludicrous by analysts like myself, who spend more than half our waking hours looking at services and costs and trying to interpret why numbers flow, the way they do.

A major problem with healthcare cost and trying to understand it stems from the source for these numbers, that define how much gets paid and by whom.

Unlike buying a large furniture set, a car, a house, or a shared part of some ridiculously high vacation land investment located in the center of the Rockies, defining a cost per unit or service, service product, belonging, or time,  becomes impossible because unfortunately, all of these costs are arbitrary and pretty much made up based upon past experiences.

The cost for undergoing an MRI for example is not determined by the amount of energy used to accomplishment.  It may in part be related to the cost for the equipment needed to perform that task.  It may even be based upon some sophisticated algorithm that takes lifespan of the unit into account, along with the cost of labor needed to use it, and the number of people who use it per given time frame, with the goal of paying it off, all or 50%, within a certain number of years.  Or you may just “ballpark” that cost by comparing it with other institutions and other costs.  Its like using the smaller car to determine how much to charge for a larger car.

To analyze cost, we need to understand cost for equipment, cost for time and human resources, and the amount of effort that goes into this overall process.

But health care has these other costs we take for granted, such as the cost for that pamphlet you were given on BC Pills, or the cost for that form the office uses to document a family’s medical history, or write out a flow chart depicting the genetics path of a rare disease in the family.  Each of these processes cost money as well, in terms of the paper object and the time needed to fill it out.  Then there’s that time spent inputting if necessary, and the time needed to find and pull it again later if need be.

Cost Paths Theory

We can use any series of pathways and logic to define health care, but in terms of evaluating the cost and time spent providing care to patients, in relation to their needs and background, the electronic medical records system many companies have may do all that is needed.

The problem with interpreting EMR data is it is not put in there in any immediately understandable, logical fashion. There is a major amount of common sense used to design the plan for this database up front, but quite soon into this project, what appeared to be a fairly easy set of data points to enter turns into an avalanche of unpredictable, uncontrolled details of findings.  Making sense of those additional data, in order to produce the most common sense, logical pathway for review of these numbers, is time consuming to say the least.

After years of reviewing various systems, there are certain methods of breaking down the data into parts to define how a patient is care for, and how the services get utilized, and human resource time gets allocated, in order to make a patient satisfied with the care process, or at least left sensing he or she has completed this particular health care “assignment” for the moment.

The following is how this pathway for care, for a single person, undergoing one visit, with a history of multiple problems, is provided healthcare.


This is a rather cumbersome, overwhelming graphical illustration depicting the flow of various activities that are engaged in with health care.  It illustrates less than 20% of the data elements needed to detail all of the healthcare processes.  Yet in terms of major tables of data, for large lists of data related to the various processes and actions that occur in health care, the items illustrated in this flowchart perhaps depict about 90% of the data most important to monitoring and evaluating healthcare practices.

This figure is also a flow chart of the care process, in which a Patient goes to a care giver’s place or setting (no matter what form), has a condition or state that has to be “treated”, requiring a number of steps in the patient-office-nurse-caregiver behaviors that ensue throughout this process, and its follow ups.  A Patient’s Visit in turn requires people interactions which then are followed by by decision making, perhaps analysis, and processes then follow to provide the service that a patient originally care for–the healthcare service, or a medicine, or a serious of tests followed by treatments, or a surgery, or a referral to the right counselor or psychologist, etc. etc. etc.

Research Questions

A series of these flowcharts will be reviewed over the next few postings to detail how these processes enable you to make sense of the system and define how it operates, where the savings can be made.


This process was developed to monitor the pre-Obamacare process, and the recent processes initiated during the Obamacare period (which apparently could be quite brief).  But any subsequent changes in this care process that ensue in the upcoming future can and should be monitored to determine if change really happens.  I am taking this approach to studying the care, beginning with the null hypothesis that states:

“The transition from PPACA or “Obamacare” to a new program will have minimal, direct impact upon the patient care Visits, Events, Procedures and related activities that ensue.”

This methodology was first developed a year ago, after the transition into a fully operational PPACA plan.  The concerns at the time related to the numbers of changes in enrollment that might ensue.

Since the reenrollment process occured late in the year, up until December 2015, it was possible to develop a model for monitoring the 2014 and 2015 patients, then assess the patient lists per quarter to determine disenrollment and new enrollment/reenrollment rates, and evaluate the numbers of patients who remained in the system.

The concerns of all insurance programs at the time was that more expensive, more risky patients dissatistfied with their services would change programs.  The goal was to identify these new enrollees and monitor their practices as unique cases loads, for comparison with reenrollees, to determine if the “sickest patients” were also the most likely to switch programs.


A related second hypothesis for this first null hypothesis is as follows:

“The actions, events, procedures and related activities engaged in as part of the health care intervention program will not show the change needed to provide reason to or explain why cost related changes are noted for the before, during and after PPACA programs.”

This is based on the added assumptions that “people are people” and in accordance with neoinstitutional theory, people in one program or group will behave like other people in another program or group, when the two groups are defined by similar means.  In this case, we are comparing patients to patients, and in particular, the old medicaid patients population to the new medical patient population, the old employee health population to the new, and the same for the two matching medicare and child health plan programs.

Using specific metrics developed for utilization rates comparisons, if there are similarities between each of the years before, during and after the PPACA, then the program itself is not responsible for any change in activities and therefore, changes in overall cost for health care at the institutional (health care facility and providers) level.  This process will also be used to assess types of visits, types of inpatient stays, groups of related diseases, and differences in health care purposeful behavior based upon age group, gender, race, ethnicity, and for the first time, religion.


The lack of evidence for an actual change in care related events, namely the numbers and types of visits by patients, suggest there may be reasons outside the actual care process that are responsible for these increases in health care cost.  So how do we detect where these changes are happening and why?

The third hypothesis states that

“The rising cost for healthcare, when healthcare processes are compared across different program-service lines, is due to causes outside the healthcare system itself, such as the insurance program, or the regulations developed as to how programs are priced, by the government and the insurance agency itself.”

The latter hypothesis may not be completely reviewable, for if the insurance programs that set the cost are at fault, as a healthcare analyst working on services and quality of care, not the business and cost-defining parts of this program, I have no direct cost evidence for how the care is priced, billed and paid for by patient, companies and other financial sources.

However, as noted in the latter “Confounders” section on this page, inferences may be made as to what some probably reasons for change might exist, outside the clinical setting.


There are specific metrics I developed for this model to measure performance of the various programs, for all services, all disease types, in terms of time, utilization, healthcare providers manpower, facilities, kinds of care needed (preventive, emergent, surgical, etc.) and theoretical cost.

This last factor is the chief reason for my development of this model.  Each step in health care, in combination with a consideration of the time needed to carry out that process, and the equipment used to perform it, result in a cost that is better off standardized, and then contrasted and compared with real costs, to determine how much certain parts of the health care process are being undercharged, and other parts overcharged.

An Example

An example of its application to a fairly young patient, in need of preventive services, but only willing to engage in one third of his/her healthcare model potential, is demonstrated.  The long term impact of this single case will be show in terms of costs for each step taken by the clinician and patient, and the actual and theoretical physical and manpower costs for each.  In theory, a new healthcare program can some how modify one or more of these events in order to best improve health, thereby lowering long term health care costs.

Due to the virtual nature of costs, we may not ever be able to prevent insurance companies and the like from making up for lost revenues by reassigning income potential along other avenues within the care system.  But this process should help to define where short cuts are taken, steps are avoided or cut in half, and processes eliminated in healthcare activities, in order to improve earnings derived from the limited resources at hand.

(for this series of teaching materials currently being developed and reconstructed for teaching purposes, see this link)



Potential confounders of this process are the impact of any social health behavior changes incurred as a result of the health care program and related insurance plans changes.

For example, disenrollment or failure to reenroll will most likely result in reduced healthcare demands.  These could in theory increase the amount of appointment time available for seeing specific physicians, which in turn could increase the compliance and activities engaged in during the practitioner-patient processes for a healthcare visit.

The “emotional” consequences of insurance program change might also change human behaviors regarding preventive health care behaviors.  In the simplest of cause and effect relationships, “reactive depression” could increase non-compliance.  Increased costs for a prescription drug could alter refill times by increasing the amount of days the patient spends using lesser amounts of the medication.  Theoretical “preventive care visits” may decrease in number, even if only by one or two visits per year, depending upon personal payments required of a new program.  The production of a voluntary enrollment process for changing program or requalifying for the new program could result in a sense of “giving up” by patients, who then delay or fail to enroll in the new plan.

Each of these provide reasons for reductions in the health care events and procedures that are reviewed after enrollment has changed for an insurance program.  Enabling a program to renew enrollment, according to predefined providers lists and schedules, mimics the HMO processes that preceded the PPACA plan initiation.  This will cause patients to change care givers and even institutions, and may force decision making changes regarding whether or not to enroll in “the full plan” or just certain parts of it.

Each of these decision making processes, on behalf of the patient, can result in reduced use in terms of numbers of patients, and reduced forms of diversity and complexity of use by the patient.  That would result in a reduction in numbers of well visits with providers devoted to one specific cause, or it could require the patient see a diabetologist for his/her diabetes, incurring a greater cost per visit for that specialty care visit, resulting in non-compliance for annual visits by a patient.  For example, severely ill patient required to partake in four separate PCP plus specialist visits per year to effective treat the metabolic syndrome diagnosis (PCP, heart, kidney, endocrine) could result in annual visit costs increasing from $40 to $80 per year to $220-$300 per year.

So confounders linked to enrollment changes have to be adjusted for using several visit-procedure metrics developed for that purpose, and a series of related general and specialty health indices generated for a review of this population, extending the use of Risk Scores well beyond the typical use of Charlson and Elixhauser scoring methods.


In the next section, more details of the above broad groups noted in the charts will be provided.