Older Age Graduates and Most Needed Health Access

Experiential Place Theme: Exclusive Versus Less Exclusive Origins

Older Age Medical School Graduates Demonstrate the Principles of Higher Probability of Most Needed Health Access Careers (Consistently 30% greater with controls) and Lower Probability of Admission

Each Additional Year of Age Indicates More Barriers to Admission (Income, Origins, Parents) That Delay Admission for more Years. The Number of Years Delayed in Admission Is also a Linear Relationship with Rural, Underserved, Primary Care, Family Medicine, and Psychiatry Career Choice

Older Age at Graduation is linked to service orientation, marriage, and fewer parents who are professionals as well as more life and health experiences prior to admission.

Older graduates are least associated with exclusive origin students, exclusive scoring students, exclusive medical schools, and exclusive career and location choices.

The typical medical school graduates for allopathic United States schools are 26 or 27 years of age at medical school graduation. These are graduates that move directly from high school to college to medical school.

Younger graduates have few or no delays in medical school admission. Some represent early admission and others completed education a year or more earlier.

Younger graduates and normal age graduates have different origins, medical schools, and career choices compared to older graduates.

Older graduates are more likely to arise from lower and middle income origins and are more likely to be delayed in admission due to barriers related to education, income, and parents. Non-traditional graduates is an older term for those 4 or more years older, but even a slight delay in medical school admission is related to different career and location choices.

Osteopathic graduates tend to be older at admission. The schools vary in admission with some schools admitting students much like allopathic schools while others admit a majority of older graduates, those 4 or more years older.

International graduates generally have 6 years of medical school rather than 4 years of college and 4 years of medical school. This results in graduation from medical school a few years earlier although an average 8 year delay in entering the US workforce results in a 23% workforce loss from delay in entry alone. Departures from the United States combined with delays in entry result in foreign origin international medical graduates with half of the workforce delivery of US graduates.

Caribbean graduates follow the US 4 year medical school pattern and have about 65 - 70% US origin admissions. Caribbean graduates represent a mix of ages and origins. The US origins allow more rapid entry to the US workforce and US origins also keep graduates in the United States rather than the 20% departure rate of foreign origin IMG.  Ross University is the largest single medical school source of US primary care and graduates the most family physicians. Older age Caribbean graduates are the only ones that are found outside of top concentrations of physicians at higher levels. Younger age graduates and normal age graduates are more likely to be found in 3400 zip codes in 4% of the land area with top concentrations at higher levels than the average US physician.

Secondary data sources can be used to link physician city of birth to a variety of demographic data. This coding is certainly related to physician origins, but is a limited indicator as it is indirect. Birth origins may reflect more about parents and how parent influences impact their physician children. What happens after birth is likely to be the same or similar life experiences for 50 – 70% of graduates compared to birth origins, but variations increase with each passing year after birth. Birth origins do not reflect higher or lower income in parents, only a place of origin with higher or lower concentrations of people, income, poverty, or whatever county or city demographic is used to link to birth origins.

Secondary databases do have direct representations of the individual physicians. The medical school of graduation, graduation date, and birth date are listed with nearly 100% complete data. Career choice is also self-designated by the physician and this leads to changes over time, but the collection and representation is consistent nationwide. Age, medical school, career choice, and practice location are specific to the physician and directly represent the individual physician.

One of the greatest problems regarding physicians is accurate data regarding their practice settings. Secondary databases are not good sources of “where a physician is right now” or even where physicians have been in the last two years. Monthly data collections with multiple zip code practice locations are more accurate. However there are methods that can improve the utility of secondary databases. A single point in time such as the 2005 Masterfile does represent a consistent cross section. Also the greatest inaccuracies tend to involve the most recent graduates. With an appropriate delay to allow physicians to complete residency training and find their way past transitions and first obligations to representative practice locations, the accuracy of secondary databases is improved. This is not the most recent graduate data, but it is the most representative of a workforce in equilibrium cross section – the most important consideration for workforce decisions for entire nations.

A study of 1987 – 2000 medical school graduates using the AMA Masterfile version for 2005 and beyond is a good balance of the most recent graduates, practice location accuracy, and representative career and location choices. This is a group that has largely completed residency and the distortions of first practice obligations or transitions to reach a representative cross section.

Physician data regarding date of birth and date of graduation can be used to compute age at medical school graduation. This is 100% available in secondary databases. In addition older age shares a linear relationship with most needed health access with greater probability of rural, underserved, primary care, and mental health careers with advancing age.

Older age is linked in other studies to service orientation, non-traditional students, more life and health experience prior to medical school, empathy, lower and middle income origins, rural origins, and truly underrepresented minority origins.^1-5 Older graduates are no-nonsense, are more likely not to have parents who are professionals, and are less likely to put up with medical education that does not appear worth their effort.⁶ Youngest age at graduation in the United States is consistently related to most exclusive schools and most exclusive origin physicians that have most urban and highest income origins with close associations with concentrations of physicians (birth in a medical school county). Each of these factors results in greater probability of admission and lower probability of most needed health access careers. Asian and foreign born are examples of combinations of concentrations in origins although those admitted of all races  and ethnicities with top combinations of concentration have top admission rates and lowest health access career choices. Older graduates and first generation to college are examples of lesser degree of concentration with increased health access career choice and more barriers to admission.

The spectrum of age at admission represents barriers to medical school admission across the entire birth to admission process in the United States of America. The most barriers related to income, education, and parents are found in older age graduates. African American, Native American, some Hispanic medical students, and rural interested medical students are all younger when they decide for a medical career and work years longer to gain admission.^{3, 5}

The fewest barriers are found in the most exclusive. Awareness of the needs of disadvantaged populations is lowest for the most exclusive and greatest for underrepresented medical students.³ The populations most connected to lower and middle income origins by birth are also most connected in awareness and in future health access career choices. They also have 4 to 6 times greater interest in serving the underserved as medical school seniors.^3-5

The rural interested seniors also represent an extension of these important health access factors. They averaged 4 years older as medical students, were more likely to be married, about half of those who were married had a rural spouse, and 61% chose family medicine. They took every opportunity possible to escape traditional academic medical training in top concentrations. They were  twice as likely to volunteer to serve the underserved during medical school and were twice as likely to do rural rotations, international rotations, military rotations, and public health experiences. They were also twice as likely to be dissatisfied with their medical school experience and listed weaknesses in doctor patient relationship, public health, community health, and ambulatory care training.⁵

Even inconsistencies in the age of graduates relative to career choice can be explained. In research schools, older age graduates contribute to highest research choice in graduates dating back to the 1940s. A focus on admission of students with life experiences involving research appears to be likely. Older graduates in research schools must be understood in the context of their past and likely future life experiences. The theme of older age graduates with more barriers also may not apply to certain states. In these states, older graduates are less likely to be found in health access careers. These tend to be states with broad distributions of income and education as indicated by education indicators. The older graduates in Midwestern states do not tend to have the same degree of increased health access career choices. These are also states that tended to have higher MCAT scores for populations of applicants as a whole. Also those admitted with higher scores also did not have the same lower choice of family medicine choice as found in schools in other states. Better distribution seem to allow better preparation for admission of a broader range of candidates as compared to states dividing into rich and poor where predominantly the privileged gain admission, there are few from middle class origins, and barriers are maximal for lower income admission. Not surprisingly health access is minimal in such states with few admitted that represent most of the population left behind, exclusive training, lower family medicine choice (selection and policy), and lowest US health policy support with least health funding to locations in most need of health access and the primary care that provides 50 – 100% of the health care in these locations.

When studying the outliers in older graduates, much was revealed about the multiple deficiencies that lead to poor cost, quality, and access.

Bivariate studies are not good indicators. Controls for type of medical training, age, and career choice are important. Older graduates, particularly in the US medical schools that supply the most physicians, are stellar in most needed health access. They are also least likely to be found in top concentrations of physicians. Older graduates are only 17% of top 20 MCAT schools that have the most exclusive admissions and increase to 30 – 40% in the schools with more normal MCAT scores. Family physicians are 28% older at medical school admission. The top health access schools in the United States average 40 – 50% older graduates.

 

	Office Primary Care in  Underserved Urban			Office Primary Care in Underserved Rural			Super Center and Major Center Concentration (over 75 Physicians at a Zip Code)
Graduation Age	< 26 Yrs	26 – 29 Yrs	Over 29 Yrs	< 26 Yrs	26 – 29 Yrs	Over 29 Yrs	< 26 Yrs	26 – 29 Yrs	Over 29 Yrs
Medical School
Osteopathic  Lower MCAT	1.0%	2.0%	2.9%	7.7%	2.4%	5.4%	67.3%	60.7%	52.1%
Osteopathic Higher MCAT	2.3%	1.7%	2.5%	1.3%	2.0%	4.0%	61.1%	59.8%	53.3%
MCAT 8.5-9.25	2.7%	2.1%	2.8%	2.7%	3.4%	5.0%	72.6%	66.3%	61.5%
MCAT 9.25-9.5	1.6%	1.8%	2.3%	2.2%	2.4%	2.7%	72.1%	71.3%	65.1%
MCAT 9.5-10	1.0%	1.4%	2.1%	0.8%	1.2%	2.0%	78.7%	73.2%	67.8%
MCAT 10-10.5	0.9%	1.2%	1.8%	0.4%	0.7%	1.5%	80.6%	75.5%	71.0%
West Coast Distributional	0.9%	2.5%	3.1%	0.2%	1.2%	2.2%	83.8%	77.7%	71.5%
MCAT 10.5-12	1.0%	1.1%	1.4%	0.2%	0.5%	0.8%	83.6%	82.2%	79.6%
Early Admit UMKC/NEOUCOM	1.6%	0.9%	1.9%	1.5%	1.6%	1.4%	74.1%	67.0%	61.9%
Historically Black	2.5%	5.0%	5.3%	1.9%	2.1%	2.4%	77.0%	64.7%	58.8%
Uniformed Services	2.3%	0.2%	0.6%	0.0%	0.4%	1.1%	43.2%	35.9%	35.5%
Puerto Rican	3.2%	4.3%	5.1%	1.6%	1.8%	3.5%	69.4%	66.2%	61.3%
Canadian	0.4%	0.6%	1.0%	0.4%	1.1%	1.8%	49.9%	52.9%	49.4%
Central American	3.3%	4.2%	5.0%	1.3%	2.2%	3.7%	72.3%	65.7%	60.3%
MS in China	1.1%	2.1%	1.8%	0.3%	0.0%	0.0%	85.9%	79.7%	87.5%
MS in India	1.6%	1.8%	1.5%	1.6%	2.1%	1.5%	76.7%	74.6%	72.8%
Distant International	1.3%	1.6%	1.7%	1.3%	1.2%	1.2%	81.1%	79.8%	76.5%
MS in Nigeria	4.7%	3.8%	0.0%	1.8%	3.0%	2.1%	70.8%	72.2%	70.2%
MS in the Philippines	4.0%	3.8%	3.8%	5.3%	4.4%	3.2%	62.2%	63.5%	58.6%
MS in Pakistan	1.6%	2.0%	0.0%	3.1%	3.5%	2.0%	71.5%	69.7%	78.0%
Caribbean MS	1.3%	1.8%	2.7%	0.9%	1.6%	2.8%	74.7%	73.0%	63.5%
Averages	1.6%	1.6%	2.2%	1.5%	1.4%	2.5%	76.2%	72.9%	66.1%

 

Exclusive training and exclusive origins result in departures from basic office primary care needs. During the 1990s graduates, internal medicine and pediatric office generalist primary care was more apparent. In the new destructive policy period, the primary care contribution is disappearing with the remaining primary care physicians more likely to be family physicians.

Age, Family Practice Career Choice, School Type by Admissions Differences, and Location Distribution

				Outside of Concentrations				Inside of Concentrations in 4% of the Land Area in 3400 Zip codes
				Marginal		Underserved
School	Older Grad	FP	Total	Urban	Rural	Urban	Rural	Major Center	Super Center	Both
Osteopathic 49-57% Older Grads	Y	Y	1135	25.7%	8.6%	7.0%	15.9%	27.0%	13.0%	40.1%
	N	Y	931	27.2%	7.5%	5.5%	10.8%	32.1%	14.5%	46.6%
	Y	N	1308	21.3%	6.3%	5.2%	8.2%	32.6%	24.2%	56.7%
	N	N	1556	17.2%	5.0%	5.3%	6.6%	34.1%	29.6%	63.8%
Osteopathic 35 – 42% Older Grads	Y	Y	1160	29.2%	11.9%	5.9%	13.2%	24.1%	13.5%	37.7%
	N	Y	1683	29.1%	11.9%	5.4%	8.7%	27.6%	15.6%	43.2%
	Y	N	1665	19.6%	9.2%	4.8%	5.9%	31.1%	27.3%	58.4%
	N	N	3422	21.3%	6.7%	3.6%	3.5%	34.4%	28.6%	63.0%
Osteopathic 24 – 33% Older Grads	Y	Y	656	29.3%	7.8%	5.6%	6.1%	29.0%	20.0%	48.9%
	N	Y	1642	32.6%	8.5%	3.5%	3.0%	31.4%	19.0%	50.4%
	Y	N	1228	19.1%	5.9%	5.0%	3.3%	33.1%	31.4%	64.4%
	N	N	3892	20.6%	4.5%	2.8%	1.7%	35.0%	33.2%	68.2%
Puerto Rico Schools	Y	Y	39	10.3%	2.6%	15.4%	2.6%	43.6%	23.1%	66.7%
	N	Y	187	11.2%	1.6%	18.2%	11.2%	32.6%	23.0%	55.6%
	Y	N	179	9.5%	1.1%	14.5%	3.4%	31.3%	36.3%	67.6%
	N	N	2373	8.4%	1.1%	15.0%	4.7%	32.8%	37.0%	69.8%
Meharry Morehouse Howard	Y	Y	121	23.1%	5.0%	14.9%	5.0%	28.9%	18.2%	47.1%
	N	Y	303	22.8%	5.3%	15.5%	7.9%	26.4%	19.1%	45.5%
	Y	N	382	20.7%	3.1%	7.9%	4.2%	26.7%	34.6%	61.3%
	N	N	1425	14.5%	1.8%	8.6%	2.9%	29.5%	39.8%	69.3%
West Coast Distributional	Y	Y	397	20.4%	7.1%	7.6%	7.1%	31.2%	25.4%	56.7%
	N	Y	834	20.1%	4.3%	8.3%	5.6%	33.8%	26.9%	60.7%
	Y	N	1165	11.6%	3.5%	5.3%	2.1%	29.8%	46.8%	76.6%
	N	N	3696	10.2%	1.9%	4.1%	1.4%	30.5%	51.1%	81.6%
Allopathic MCAT 8.5-9.25	Y	Y	1060	20.9%	13.3%	7.7%	17.5%	24.5%	14.5%	39.1%
	N	Y	2559	24.7%	13.9%	6.5%	12.3%	25.9%	15.0%	40.9%
	Y	N	3380	13.7%	5.0%	5.9%	6.1%	31.1%	36.7%	67.8%
	N	N	11080	12.6%	4.1%	5.1%	4.6%	32.5%	39.7%	72.2%
Allopathic MCAT 9.25-9.5	Y	Y	1081	24.2%	11.7%	8.5%	10.9%	23.2%	19.7%	42.9%
	N	Y	3050	23.3%	8.6%	6.2%	11.5%	27.8%	20.0%	47.7%
	Y	N	4470	14.4%	4.5%	4.8%	3.9%	27.0%	42.7%	69.7%
	N	N	16277	12.9%	2.9%	4.0%	3.2%	29.4%	45.8%	75.2%
Allopathic MCAT 9.5-10	Y	Y	1825	23.3%	12.3%	6.4%	8.9%	24.2%	22.3%	46.5%
	N	Y	5432	26.5%	10.8%	5.4%	6.5%	27.6%	20.6%	48.3%
	Y	N	8841	14.6%	4.0%	4.7%	3.1%	29.4%	42.2%	71.6%
	N	N	38523	13.8%	2.7%	3.2%	1.6%	29.7%	47.1%	76.8%
Allopathic MCAT 10-10.5	Y	Y	1256	24.5%	13.6%	6.1%	7.1%	25.5%	21.3%	46.7%
	N	Y	3780	26.7%	14.0%	4.9%	4.5%	26.6%	20.8%	47.4%
	Y	N	6241	13.9%	3.9%	3.1%	1.8%	28.7%	46.9%	75.5%
	N	N	27768	12.3%	2.6%	2.9%	1.2%	28.9%	50.2%	79.1%
Allopathic MCAT 10.5-12	Y	Y	547	20.8%	7.9%	6.8%	6.2%	29.3%	26.9%	56.1%
	N	Y	1829	21.1%	7.8%	7.9%	5.4%	28.4%	26.9%	55.3%
	Y	N	4896	10.8%	2.0%	3.1%	1.7%	22.3%	59.0%	81.3%
	N	N	28286	9.5%	1.6%	2.6%	1.0%	24.2%	59.6%	83.8%
Allopathic Early Admission Northeast Ohio, U of Missouri KC	Y	Y	31	25.8%	16.1%	6.5%	9.7%	32.3%	6.5%	38.7%
	N	Y	261	31.8%	6.9%	5.0%	8.8%	27.6%	18.0%	45.6%
	Y	N	151	19.2%	6.6%	4.6%	3.3%	30.5%	33.8%	64.2%
	N	N	1754	14.7%	3.0%	3.2%	1.4%	30.4%	46.3%	76.7%

The older graduates, graduates choosing family practice, and graduates of schools with less exclusive admission are consistently more likely to distribute outside of super center and major center concentrations to locations where 65% of the US population is found.

Logistic regression equations with multiple origin factors (rural, quartile of income, birth in a county with a medical school), health access career choice (family medicine), and exclusive training (allopathic private or top 30 MCAT school), older age graduates still are 30% to 40% more likely to be found where needed.

The percentage of older graduates, those 4 or more years older at medical school graduation, increases in the most underserved areas. The medical schools with the highest levels of older graduates consistently graduate more to the most needed health access careers. The relationship between age and most needed career choice is linear. Increasing age is associated with increased distribution.

Source: Medical School Admissions Requirements, 2002-2003, (Table 5-B, AAMC)

The statistics are different for some allopathic schools and also osteopathic schools.

Again those less likely to gain admission, when they do gain admission, are most likely to be found where most needed.

Graphic on Age and Type of School

Most international medical school graduates enter at early ages. Exclusive US medical schools have more medical students that are younger. Osteopathic and Caribbean schools tend to have US graduates that are somewhat older. The North American schools in Canada, Mexico, and Central America graduates are a mix of different 4 and 6 year medical school types.

Graphic on Older Graduates and Most Needed Career Choice

The median age at graduation also varies consistently with the most exclusive careers graduating at the youngest ages and more likely to arise from the highest income, most urban counties. Also it is quite remarkable that medical school admission committees consistently admit the same 100 to 150 students that have the same MCAT score averages (adjusted steadily up for national increases) and GPA and age median at graduation.

Age Means for US MD and Osteopathic Grads and Family Practice Graduating from Medical School 1987 - 2000

	All	FPGP
DO	46.61	48.49
MD	48.06	47.48

 

As osteopathic graduation numbers increase and as osteopathic graduates move away from family and general practice, the average age of osteopathic family physicians increases. In the allopathic graduates the 1990s policy changes resulted in peak family medicine choice with younger graduates increasing relative to past class years of family physicians. As choice of family medicine heads to lowest percentages in allopathic graduates, family physicians will increase in age means.

Older age generally reflects lower production with insufficient graduates to meet health care demand, but there are exceptions. Nurse practitioners are older and commonly discuss older age relative to aging workforce but with one big difference. Nurses are older to begin with. Nurses graduate from nursing school now at an older age of 31 years. Even with fewer years as a registered nurse, nurse practitioners enter in their late 30s to primary care. Nurse practitioners at their average age of nurse practitioners in the mid to late 40s, nurse practitioners have only had 10 years as a practitioner while family physicians have served two decades in practice and have two decades remaining. When adjusted to actual primary care delivery (volume, primary care retention, lower level of activity with more part time and inactive), nurse practitioners at the age mean will have only delivered 2 Standard Primary Care years with another 2 years remaining (not counting departures in the next decades, not counting 15% found in administrative careers that also tend to be older). Family physicians at their age mean listed above in the mid to late 40s will have delivered 15 Standard Primary Care Years with 15 remaining.

Osteopathic 2004 Graduates and the Percentage Major or Strong Career Influence

Characteristics	Primary Care	Not Primary Care
Intellectual Content	77.0%	86.0%
Dealing with people	88.0%	66.0%
Prestige and income	10.0%	32.0%
Lifestyle	58.0%	67.0%
Technical skills	25.0%	61.0%
Role models	59.0%	55.0%
Academic environment	39.0%	50.0%
Research	18.0%	31.0%

Survey of Osteopathic Medical Students and Residents 2004 AOA 

Three Career Divisions Related to Age at Graduation

The career choices of the extremes of usual age medical students were compared: those age 25 or 26 at graduation were compared to those age 29 or 30 years of age and three groups were obtained: a career group with increased choice as age increased at graduation, one with little change, and a group that decreased with older age at graduation. Comparing students across the usual age range from 25 to 30 years of age illustrates the differences in career choice by age. For these determinations the percentages noted to be active and practicing in the 2005 Masterfile are compiled by career and location regarding the 1987 – 1999 graduates. The career choices are also compared to medical school class characteristics for mean age of for each medical school for the 1987 – 1999 graduates of the school and for the average MCAT score for the school for the 2000 – 2003 graduates. A positive correlation with age means that a career choice such as family medicine increases with increasing age of a medical school class. A negative MCAT correlation means that percentage of students at a school choosing a career such as family medicine decreases as the MCAT score of the school rises. Both appear to be measures of the selectivity and exclusiveness of the medical school and both relate to important measures of physician distribution and workforce. Finally the author prepared a rating scale for each career in two dimensions and scored each from 0 to +++ for General/Primary Care/Behavioral and for Procedural/Technical/Subspecialty. The table was ranked and grouped by correlation with mean age of medical school graduates. The correlations shown for MCAT and mean age involve the typical 109 medical schools.

 

Characteristics of Medical Student Careers Related to Age and School MCAT

	Average % Choice	Average % Choice		Typical 109 Medical School Correlations		Increase from Birth Origin County < 125 PPSM to county > 2500 PPSM
Career Choice for 1987 – 1999 graduates	Age 25-26	Age 29-30	Per Cent Change By Age	Mean Age Grads	MCAT of School	Exclusive Origin Ratio	General, Primary Care, Behavioral, People Focus	Procedural, Technical, Subspecialty (Higher Income)
Group I	Lower	Higher	Increased	Older	Lower	Least Exclusive Origins	+++	No
Office Family Med	7.0%	12.4%	88.7%	0.64	-0.63	55%	+++
Office Rural FM	2.4%	3.4%	43.0%	0.56	-0.63	Decrease	+++
Rural Careers	7.1%	13.4%	89.0%	0.45	-0.67	20%	Mix	Mix
Child Psychiatry	0.7%	1.1%	48.0%	0.37	-0.44		+++
Office Rural Peds	6.9%	5.9%	-14.0%	0.36	-0.57	<50%	+++
Rural Internal Med	0.9%	1.7%	95.0%	0.35	-0.53	<50%	+++
Rural General Surgery	0.3%	0.7%	119.0%	0.32	-0.56	<100%	+++
Anesthesia	5.7%	6.6%	17.0%	0.27	-0.36	218%	+	++
Off PC Underserved	2.5%	3.5%	38.9%	0.23	-0.31	<33%	+++
Emergency Medicine	5.4%	6.0%	11.0%	0.22	-0.09*	288%	+++	+
Gen Psychiatry (older, urban origin)	2.5%	4.2%	66.0%	0.18	0.00*	346%	+++
Group II	Mix	Mix	Mix	Neutral	Neutral	More Exclusive Origins	General, Primary Care, Behavioral	Procedural, Technical, Subspecialty
General Pathology	1.0%	1.7%	70.0%	0.10*	0.06*	195%	++	++
Med-Peds (only 20% of early grads left)	1.4%	0.9%	-35.3%	0.02*	-0.48	Not Able	+++	+
Obstetrics-Gyn	6.9%	6.8%	0.0%	0.02*	-0.40	239%	++	+
Office Internal Med (for those remaining)	17.1%	16.3%	-5.0%	-0.03*	-0.01*	307%	+++
Allergy Immunology	0.6%	0.3%	-53.0%	-0.11*	-0.26		+	++
Pulmonary	0.3%	0.2%	-49.4%	-0.11*	-0.05*	Changes	++	+++
Pediatrics (for those remaining)	8.9%	7.4%	-16.0%	-0.14*	0.20	322%	Mix	Mix
Radiation Oncology	0.8%	0.7%	0.0%	-0.15*	0.22		+	++
General Surgery	5.5%	4.9%	-10.0%	-0.16*	0.27	203%	++	++
General Internal Med	16.3%	15.4%	-6.0%	-0.17*	0.47	307%	Mix	Mix
Group III	Higher	Lower	Decrease	Younger	Higher	Most Exclusive Origins	General, Primary Care, Behavioral	Procedural, Technical, Subspecialty
Plastic Surgery	1.1%	0.7%	-35.4%	-0.30	0.43	468%		+++
IM Residency Grads	24.2%	22.3%	-7.9%	-0.32	0.51	>350%	Mix	Mix
Diagnostic Radiology	5.0%	3.4%	-32.2%	-0.32	0.05*	346%	+	++
Office Pediatrics	9.4%	8.0%	-15.0%	-0.35	0.14*	322%	+++
Dermatology	2.1%	1.4%	-34.7%	-0.37	0.62	341%	+	++
Hematology Oncology	0.9%	0.6%	-26.3%	-0.37	0.53		+	++
Neurology	1.5%	1.4%	0.0%	-0.37	0.41	492%	+	++
Pulmonary Critical	1.2%	0.5%	-58.2%	-0.42	0.34	Changes		+++
Urology	1.3%	0.7%	-41.3%	-0.42	0.41	389%		+++
Neurosurgery	0.9%	0.6%	-38.1%	-0.42	0.61	492%		+++
Thoracic Surgery	0.6%	0.4%	-27.8%	-0.43	0.45	477%		+++
Ophthalmology	4.0%	2.1%	-46.6%	-0.44	0.59	391%		+++
Otorhinolaryngology	1.7%	1.2%	-30.2%	-0.48	0.55	309%	+	+++
Radiology	1.3%	0.9%	-29.0%	-0.51	0.18*	346%	+	++
Gastroenterology	2.0%	0.9%	-56.1%	-0.52	0.15*	579%	+	+++
Orthopedics	2.9%	2.7%	0.0%	-0.53	0.66	289%	+	+++
Cardiology	3.0%	1.5%	-51.6%	-0.57	0.48	493%	+	+++

* Other than marked with an asterisk, the correlation is significant at p < .05. Most are significantly higher. Rural pediatrics choice is higher in the older age ranges. Caution is needed in interpreting the medicine pediatrics data. Only 1580 remain in the Masterfile of 3100 who matched into medicine pediatrics for 1987 - 1999. Those remaining are older and are from medical schools with higher levels of family medicine, primary care, and distribution. .

The specialties in Group I increasing with older age at graduation from medical school involve general specialties or those with broad scope. These include underserved primary care, family medicine, rural practice, and behavioral modes of health care. The focus involves people. Generally lower income is noted. Anesthesia choice was very different during this time period compared to most time periods due to managed care 1990s impacts. Rural careers are more likely to arise from rural origins as well as older age. Rural origins are associated with lower and middle income origins, explaining some components of underserved, family medicine, and primary care.

The middle ground in Group II includes careers with less change with age. These also include specialty choices such as internal medicine and pediatrics where students move on to specialties later. This is a mix of those interested in specializing and those interested in more general careers. The careers decreasing with older age involve technology and subspecialization. 

The Group III physicians clearly are likely to be more exclusive in age at graduation (younger), more exclusive in school (MCAT score), more exclusive in origins (most urban, highest income birth county), are least oriented to people careers, and are most oriented to procedural and technology focus. They are rewarded with the highest income with the most lines of revenue and the highest level of reimbursement in each line. They are also some of the most dissatisfied physicians despite the highest income, the most support personnel, the least people contact, the highest MCAT, the highest Board Scores, the most prestigious colleges and medical schools, and the most privileged parents.

Basic health access is entirely about Group I with origins spread across the US population, people focus, less focus on technology, lowest salaries, lowest support personnel, and generally higher physician satisfaction levels. The geriatric and pediatric physicians were the most satisfied. Family physicians were a neutral set point. More exclusive careers were less satisfying.

The careers increasing with age most involved behavioral and primary care and were more general in scope.  The specialties with broader scope were more likely to be found in rural areas. The careers preferred by older graduates were more likely to involve the biopsychosocial model.

The career choices declining the most with age are the ones most associated with science, technology, and procedures. Careers declining with age tended to involve the biomedical model. The top rated subspecialties have close associations with younger age, higher MCAT, and elite medical schools.

Older age in allopathic United States medical schools shares a positive correlation with needed health access career choices. The average Medical College Admission Test score of a medical school shares a strong and negative 0.4 to 0.5 correlation with needed health access career choice using median age of graduating class or the percentage of graduates 4 or more years older. 

Studies and reports by the Association of American Medical Colleges have not included comparisons of people orientation, MCAT scores, communication skills, and parent income levels together with career and location choice. Currently selection focuses on higher MCAT and higher GPA with both correlated with one another. The introduction of communication skills is important as a major factor known to impact physician quality.⁷ However it is not a difficult prediction that the students that will do poorly in people orientation and communication skills are going to be the biomedical types that dominate medical school admission and exclusive career choice. Their powerful parents are also not likely to be happy about this situation as with past situations where minority or rural origin students with higher probability of health access careers were given preference.

Few understand that standardized tests are flawed. At higher levels of testing such as MCAT, the test has been documented as biased for even small changes such as gender, much less major differences across combinations of top concentrations. The highest income, most urban, children of professionals set the standard for the MCAT and any child more normal will have a lower MCAT score to some degree just because they are more normal and less exclusive. Increasing MCAT scores also indicate ever higher combinations of concentration for matriculants as well as decreasing lower and middle income admissions as is documented in the past decade.

Individual studies have demonstrated the relationship of family medicine choice to parent income.⁸ Birth origins clearly indicate the impact of origin to family medicine, rural practice, and underserved career choices. The factors related to higher MCAT are related to lower age, exclusive origins, and exclusive medical school.

Service orientation, people orientation, primary care, family medicine, fewer parents who are professionals or physicians, most needed health access, and older age go together.^{1, 2, 6, 9} Older age graduates boost most needed health access by 30% controlling for origins, training, and career choice.^10-12

Board scores are also negatively correlated with needed health access careers using national data on the specialty. Of course the same results would be found using SAT and ACT or other standardized tests although the MCAT test is designed for the most exclusive group. The students doing best in any standardized tests are those that determine the standard being tested. This was a point that failed to come across in the various court deliberations that have involved standardized tests. Supreme Court Justices of most exclusive origins and trained predominantly in the most exclusive law school, may have a difficult time seeing the problems of standardized testing or the destructive nature of admissions based on MCAT ranking for interview or for final admission. Use of the MCAT to assure sufficient academics is fine. But final selection of physicians that will have 45 year careers long after the MCAT test should be based on what is important to patients and health care – communication skills, people orientation, the ability to defer self in favor of patients, character, diligence, and more. There are far too many important qualities and characteristics of physicians to allow limitation of the admitted pool by a small minority of top GPA and top MCAT that are less likely to be able to fit the real criteria most important for physicians.

The MCAT is clearly a speeded test. This means that there is bias against those who take even a few seconds longer to process each multiple choice test question. Those that take longer are consistently the students who are less than the most exclusive. Also higher MCAT scores have never been associated with better quality as a physician. The MCAT as a predictor fades even in the first year or two of medical school. Of course researchers can pick and choose the studies that they use for references.

In osteopathic schools such as A T Still SOMA, there is no correlation between MCAT and first board exam score. Overall at SOMA and in studies of all osteopathic schools, the highest first board score correlations involve the GPA for the first two years of medical school.¹³ It is quite refreshing to know that what medical students do and what medical schools do is more important than the parent influences that shape top MCAT scores.

It is possible to cherry-pick studies to document MCAT correlations. People appear to want to believe that the MCAT results in higher quality physicians. Having an “objective” measure could simplify admissions, a most complex task. But the MCAT is not related to higher quality and is not truly objective. It can appear to have higher correlations, but this is setting dependent. For example a June 2009 article regarding the MCAT, board scores, and individual medical student characteristics referred to the only study to demonstrate a higher (0.44) correlation between MCAT and board scores.¹⁴ A better reference would have been complete populations. In the definitive study of 18 osteopathic schools, the correlation of MCAT to first board was much lower. It is not a surprise that the school with the 0.44 correlation is in New York, an exclusive location with highly competitive admissions likely to result in the most exclusive. Even with 0.4 correlations, only 16% of the variance is explained. The correlation between board scores and performance during the first two years of osteopathic medical school is much higher in the 0.6 to 0.8 range. This explains 40 – 60% of the variance. Even the first test of a medical school class generally explains 50% of the variance in the performance of the first two years of medical school. This makes sense as students demonstrate their abilities in medical school directly rather than a limited predictive ability using admissions variables. Osteopathic students also tend to have relatively higher MCAT with lower GPA or lower MCAT with higher GPA. The same is seen in lower MCAT ranking allopathic public schools. It is not a surprise that these schools tend to have lower correlations between board scores and MCAT.

 

Should students demonstrate their actual ability in medical school or in situations much like medical school or should students be chosen by a very limited standardized test performance prior to admission that has little bearing on performance as a physician?

One method results in exclusive students and exclusive careers and locations. One method is more balanced in selecting those that can still choose the most needed health access careers.

While most deans and admission committees are fully aware of the potential for academic difficulty when admitting students that are more normal, few are aware of other consequences that must be considered. When students do not have multiple risk factors, academic difficulty is not a major concern. Appopriate in admission is more normal, not extremes of lower scores, lower grades, and most disadvantaged origins

Communication skills areas have been demonstrated as clearly linked to poor performance as physicians yet communication skills testing has been delayed significantly by the Association of American Medical Colleges. Experts at AAMC were charged with the task of communication skills testing, but were chosen based on abilities in computerized test implementation. Not surprisingly the AAMC computerized the MCAT (and has yet to understand how this may be a barrier to health access or who may be adversely impacted or favored by computerization) and did not make progress with communication skills. Now that some competition may be developing for communication skills testing, the AAMC is finally making some efforts.

As physicians get more exclusive in origins and more narrow in biomedical focus, there is a good probability that physicians will actually have a more difficult time with the humanistic dimension that has been a mainstay of the doctor patient relationship as long as there have been physicians.

The top experts such as those that have supervised the test (Ellen Julian former VP of AAMC in charge of the MCAT) have readily admitted that the MCAT cannot separate students unless there is too little time for too many questions. In court she testified that more time given improves the test scores in some students but not in others. The testimony was somewhat vague. While it is true that some improve and some do not, this is not the full representation of the data. In studies of the MCAT regarding speeded bias contracted by AAMC, more time allotted for testing resulted more often in higher scores.

Speeded bias is likely for all that are not the most exclusive. While many assume that the ability to read and comprehend rapidly is important with so much material to cover during medical school, what is often more important is study skills, the ability to organize material, working well in a study group, and maintaining a high motivation for a medical career. One of the major barriers during medical school is stress not related to academics such as life changes, a baby, a death in the family, the need to address family of origin needs, etc. The MCAT says nothing about most of the areas more important in medical school performance. Most exclusive origin students can be a great disappointment as they can have higher scores and low motivation for a medical career and do poorly in medical school.

The MCAT also has a consistently strong and negative correlation with underrepresented minority students. The most normal students with lower and middle income origins will have a lower MCAT just because they are more normal. Although rural, older, students from less prestigious colleges, and underrepresented minority students do have lower scores, a lower MCAT score cannot predict the individuals that will do well or not. Sadly because they are more normal and less than the most exclusive, they are likely to have a rate of academic difficulty of 4 to 10%. In a practical sense with 20 – 30 admitted in a class, 10 will do just as well as other students, 10 will struggle but complete studies, and 10 will remain at the bottom of the class at risk of academic difficulty. What is consistent is not a lower MCAT score, but the presence of numerous risk factors such as more barriers to admission (underrepresented, from a high poverty high school zip code, economically disadvantaged, first generation to college), stress factors, lower biological sciences MCAT, and lower science GPA. When student after student with a 6 or 7 or 8 bioscience MCAT subscore does well in medical school yet is excluded from admission or even interview from most medical schools, there is a problem. One or two risk factors is not a problem, particularly when students have no other risk factors or have strengths in the humanistic dimension. A process of admission that only admits those without any risk factors, basically excludes students with lower and middle income origins or the medical students most likely to choose needed health access careers.

It is important to realize that as many as 25% of the physicians admitted in the past decades have bottom quartile communication skills, a factor that is related to lower physician quality in adverse patient events. Also adverse events with health care team members and administration have not been studied. Schools would do well to excluse those with low level communication skills and probably lowest skills in the humanistic dimension. This would include dismissal during medical school. Of course this is something not even yet considered in medical education but clearly indicated to protect the public.

But of course even those at highest risk for poor communication skills may not have adverse events. This is the same comparison as those at highest risk for academic difficulty where the adverse event of failure is actually only 10 – 15% and where there is no increased risk of poor quality as a physician. Then there is the potential that most exclusive students may have difficulties in a number of relationship areas when caring for those most different. United States medical education has failed to study some of the most important areas related to cost, quality, and access.

 

 

1.            Madison DL. Medical school admission and generalist physicians: a study of the class of 1985. Acad Med. Oct 1994;69(10):825-831.

2.            O'Connor SJ, Trinh HQ, Shewchuk RM. Determinants of Service Orientation Among Medical Students. Available at www.sba.muohio.edu/management/mwAcademy/2000/38c.pdf. Oxford, OH: Miami University Farmer School of Business; 2000.

3.            Association of American Medical Colleges. Minority Students in Medical Education: Facts and Figures XI Available at https://services.aamc.org/Publications/showfile.cfm?file=version12.pdf&prd_id=89&prvid=87 Accessed April, 2003. Washington DC 1998.

4.            Association of American Medical Colleges. Minority Students in Medical Education: Facts and Figures XIII Available at https://services.aamc.org/Publications/showfile.cfm?file=version53.pdf&prd_id=133&prv_id=154&pdf_id=53, Accessed July 2006. Washington DC 2005.

5.            Bowman RC, Schuchert M. Rural Interested Senior Medical Students. AAMC Graduation Questionnaire. Washington DC: Data from the 1995 Association of American Medical Colleges Graduation Questionnaire; 1998.

6.            Harth SC, Biggs JS, Thong YH. Mature-age entrants to medical school: a controlled study of sociodemographic characteristics, career choice and job satisfaction. Med Educ. Nov 1990;24(6):488-498.

7.            Tamblyn R, Abrahamowicz M, Dauphinee D, et al. Physician scores on a national clinical skills examination as predictors of complaints to medical regulatory authorities. Jama. Sep 5 2007;298(9):993-1001.

8.            Cooter R, Erdmann JB, Gonnella JS, Callahan CA, Hojat M, Xu G. Economic Diversity in Medical Education. Evaluation and the Health Professions. September 2004;27(3):252-264.

9.            Xu G, Veloski JJ, Barzansky B. Comparisons between older and usual-aged medical school graduates on the factors influencing their choices of primary care specialties. Acad Med. Nov 1997;72(11):1003-1007.

10.          Bowman RC. Logistic Regression and Rural Practice Location. In: Proceedings, Association of American Medical Colleges 2007 Workforce Conference; 2 May; Washington DC, 2007.

11.          Bowman RC. They really do go. Rural Remote Health. Jul-Sep 2008;8(3):1035.

12.          Bowman RC. The Basic Logistic Regression Tables: Taxonomy, Themes, Theories of Experiential Place and Basic Health Access.  http://www.ruralmedicaleducation.org/basichealthaccess/taxonomies_themes_theories.htm.

13.          Baker HH, Foster RW, Bates BP, et al. Relationship between academic achievement and COMLEX-USA Level 1 performance: a multisite study. J Am Osteopath Assoc. Apr 2000;100(4):238-242.

14.          Sefcik DJ, Prerost FJ, Arbet SE. Personality types and performance on aptitude and achievement tests: implications for osteopathic medical education. J Am Osteopath Assoc. Jun 2009;109(6):296-301.

 

http://www.ruralmedicaleducation.org/basichealthaccess/taxonomies_themes_theories.htm

 

www.basichealthaccess.org

 

www.physicianworkforcestudies.org

 

www.ruralmedicaleducation.org