The Majority of DSM Diagnoses Are Never Used...

 

The landscape of medical and psychiatric diagnoses that are actually used by clinicians has always interested me.  Diagnostic classifications like the DSM and the ICD are generally used for the purpose of billing and generating statistics.  There is also an implicit research function that is probably why the number of diagnoses are so expansive.  I wrote a brief comment about this on my other blog almost exactly 3 years ago.  In a study of 1,260,097 psychiatric diagnoses reported from hospital care between 2001-2007 only 16 or 4.2% of the available diagnoses accounted for 50% of the reported activity (1).  Forty-nine diagnoses accounted for accounted for 75% of the activity and 108 diagnoses accounted for 95% of the activity.  Of the total diagnoses available most were used infrequently if at all.  In a separate abstract, 32 diagnoses were not used at all and 121 diagnoses were used in less than 0.1% of cases (2).  This is an important issue that I intend to use in further posts about diagnostic reasoning in psychiatry.  This is intended as an update and moving the concept over to my main blog.

The first question when it comes to either DSM or ICD diagnoses in different clinical settings is – how many are there?  In the case of the DSM – I personally counted the diagnoses and came up with 281 diagnoses using the methods outlined in that post.  Since then, I have encountered a reference that lists the total diagnoses as 245 (3).  In an earlier DSM-III study of 11,292 general psychiatric admission 296 of 329 available diagnoses were used and the 9 most frequent accounted for 35.8% of all diagnoses (4).

Surveys of Psychiatric Diagnoses Used In Practice
N	Classification	Used/Available (%)	Skew
11,292 adults	DSM-III	296/329 (90%)	73% of diagnoses were from 6 diagnostic categories with major depression the predominate category at 23%
214,206 adults	ICD 9/10-CM	----	mood disorders (22%), anxiety disorders (21%), and substance use disorders (16%) together accounted for the majority of documented psychiatric diagnoses
13,684,154 children and adolescents	ICD 9/10 – grouped as 13 diagnostic groups and 1 other	-----	Diagnostic groups were trauma/stressor-related disorders (27%), anxiety disorders (19%), and depressive disorders (17%)
7,076 adults	DSM-III-R	------	41.2% of the adult population under 65 experienced at least one DSM-III-R disorder in their lifetime, 23.3% within the preceding year. Depression, anxiety, and alcohol abuse and dependence were most prevalent
1:  Mezzich JE, Fabrega H Jr, Coffman GA, Haley R. DSM-III disorders in a large sample of psychiatric patients: frequency and specificity of diagnoses. Am J Psychiatry. 1989 Feb;146(2):212-9. doi: 10.1176/ajp.146.2.212 2:  Barr PB, Bigdeli TB, Meyers JL. Prevalence, Comorbidity, and Sociodemographic Correlates of Psychiatric Diagnoses Reported in the All of Us Research Program. JAMA Psychiatry. 2022;79(6):622–628. doi:10.1001/jamapsychiatry.2022.0685 3:  Mojtabai R, Olfson M. Trends in Mental Disorders in Children and Adolescents Receiving Treatment in the State Mental Health System. J Am Acad Child Adolesc Psychiatry. 2025 Aug;64(8):906-920. doi: 10.1016/j.jaac.2024.08.008. Epub 2024 Aug 28. PMID: 39214290. 4:  Bijl, R., Ravelli, A. & van Zessen, G. Prevalence of psychiatric disorder in the general population: results of the Netherlands Mental Health Survey and Incidence Study (NEMESIS). Soc Psychiatry Psychiatr Epidemiol 33, 587–595 (1998). https://doi.org/10.1007/s001270050098

 I have listed several additional surveys of diagnoses in various samples.  Comparison across studies is complicated by the classification system used and whether specific diagnoses are counted or diagnostic groups.  If only groups are counted it is more difficult to illustrate the skew by weighting.  Large healthcare systems have these statistics but I am not aware of any of that data being published.  Having worked for one of those systems the data is often considered proprietary.  The data would also be affected by the clinical populations being treated.  I would expect safety net hospitals to have a much higher percentage of disability associated diagnoses than private hospitals.  I would expect the same skew between acute care settings (inpatient units and acute psychiatric services) to have a different distribution of diagnoses than outpatient clinics.  Of the 3 studies that looked at this issue above using DSM criteria – most DSM diagnoses are used infrequently if at all.

What about the criticism of the proliferation of diagnoses?  I expect to see the usual discussion of this issue as the DSM-6 is hyped as a controversial topic over the coming years.  We already know the answer to the question but everyone will need to pretend that we don’t. By my count the DSM diagnoses peaked with the DSM-IV.  A lot of the controversy about diagnostic proliferation will start by saying the DSM-I had 106 diagnoses in 1954 and that number has more than doubled.  Nobody will say that most clinicians are using a set of diagnoses so limited that they have the numerical codes memorized so they do not have to keep looking them up.  

A comparable look at the ICD shows that it started out in in 1893 as the International List of Causes of Death (or the Bertillon Classification of Causes of Death).  There were 44, 99, or 161 codes that could be used depending upon the reporting capabilities of the country.  The 161-code version became the ICD and in 1898 the American Public Health Association (APHA) recommended that Canada, Mexico, and the US adopt it and revise it every 10 years based on advancements in medical knowledge.  The current version ICD-11 has 55,000 codes up from the previous version (ICD-10) 14,000 codes.  

Any comparison of numbers of diagnoses is problematic for several reasons.  The authority proposing the classification system is averse to reporting them. That is true whether it is the DSM or the ICD. When I counted them, I provided the methodology and you can replicate it yourself.  With the DSM there are occasional isolated counts close to mine – but no explanations.  With the ICD – things are more complex and estimated range from 10,000 – 15,000 diagnoses that would be recognized as unique.  In the ICD-11 those diagnoses are included with other biomedical terms in the underlying Foundation of the ICD.  The Foundation is technically a semantic database of terms including symptoms and other findings. 

Before getting into how these codings work relative to diagnoses – a brief introduction to ICD coding terminology since it is impossible to separate out what physicians typically consider diagnoses.  In the example below, I have produced a hierarchical tree diagram that is considered the basis for the ICD.  In the example I am following how an episode of recurrent depressive disorder-severe without psychotic features is coded.  The top category is the grouping of all medical disorders into 28 categories.  The next group is all mental, behavioral, and neurodevelopmental disorders grouped into 24 categories.  From there a mood disorder group, depressive disorder group and recurrent depressive disorder group follows.  The final grouping is the variant of 15 recurrent depressive disorder possibilities that we are looking for.  In ICD jargon, that final group is called a leaf code because it is the ultimate result of the hierarchy and it cannot be split any farther.  The branching above that level is called stem codes.   

 

A more interesting comparison is how the diagnostic codes in the rest of medicine have increased.  

Version	Approximate Leaf Codes	Notes	References
ICD-10 (WHO)	~10,607	Base international version	[1]
ICD-10-CM (US)	~71,932	US clinical modification with extensive granularity	[1]
ICD-11-MMS	~14,622	Moderate increase over ICD-10; post coordination expands expressivity	[1]
ICD-11 Foundation	Much larger	Includes 5,500+ rare diseases; serves as semantic knowledge base	[2-3]
1:  Fung KW, Xu J, Bodenreider O. The new International Classification of Diseases 11th edition: a comparative analysis with ICD-10 and ICD-10-CM. J Am Med Inform Assoc. 2020 May 1;27(5):738-746. doi: 10.1093/jamia/ocaa030. PMID: 32364236; PMCID: PMC7309235. 2:  Feinstein JA, Gill PJ, Anderson BR. Preparing for the International Classification of Diseases, 11th Revision (ICD-11) in the US Health Care System. JAMA Health Forum. 2023;4(7):e232253. doi:10.1001/jamahealthforum.2023.2253 3: Chute CG. The rendering of human phenotype and rare diseases in ICD-11. J Inherit Metab Dis. 2018 May;41(3):563-569. doi: 10.1007/s10545-018-0172-5. Epub 2018 Mar 29. PMID: 29600497; PMCID: PMC5959961.

   

The table shows a direct comparison between the ICD-10 and ICD-11.  The conclusion is that there has been a moderate increase in codes.  Leaf codes can undercount and overcount the diagnoses and are not necessarily strict representations of diagnoses.  For example, a code of type 2 diabetes mellitus can generate many additional codes depending on the complications.  The only equivalent in the DSM are the modifier codes.  Medicine can also code symptoms rather than a specific diagnosis – so those codes like “neck pain, cough, constipation, etc) also generate codes that have no DSM equivalent.  There is residual or not-otherwise-specified (NOS) codes in the ICD that meet no diagnostic criteria.  The DSM-5-TR has replaced NOS codes with other specified disorder or unspecified disorder that are probably not much better.  The ICD-11 added complexity codes for severity, histopathology and other features to increase specificity.

The structure of the ICD is relevant to counting diagnoses.  The basic a hierarchical tree structure that can be viewed at the following link.  In this case the diagram illustrates the hierarchy Mental, Behavioral, or Neurodevelopmental disorders (category) -> Mood Disorders (3) -> Bipolar Disorder -> Bipolar Type 1 Disorder (16) -> Bipolar type I disorder, current episode manic, without psychotic symptoms (32) -> Bipolar type I disorder, current episode manic, without psychotic symptoms, with prominent anxiety symptoms (32).  The numbers in parentheses indicate the total branching of the hierarchical tree diagram.  The branching is graphically represented in the center panel.

A comparison of leaf codes in the DSM is possible by estimating leaf codes as 3-5-digit total billable codes.  That would include about 350 leaf code like endpoints and 150 environmental codes for total of about 500.  That is only about 3% of the total ICD-11 codes in the above table – a number made more significant by the fact that the DSM includes diagnoses that the ICD codes in other categories – most notably neurocognitive disorders.   

In conclusion – all of the controversy about the proliferation of diagnoses (or codes) in the DSM as excessive does not match the reality of how diagnoses in general have increased in the rest of medicine. If anything, it seems to be lagging.  It also misses the point why this happens in the first place as it was well put by the American Public Health Association in 1898 – to revise the ICD every 10 years “based on advancements in medical knowledge.”   

 

George Dawson, MD, DFAPA

 

Supplementary 1:  Leaf code approximation:  I counted all of the diagnoses listed in the chapter "Numerical Listing of DSM-5 Diagnoses and Codes (ICD-10-CM)” Total codes listed in that appendix are 760 but it is a mapping of DSM diagnoses onto the ICD-10 and that is not an exact match.  As a result, there are 148 duplicate codes bringing the total down to 612.  The list also contains parent or sub-stem codes such as F79 (Unspecified intellectual disability) that requires an additional digit to become a leaf code.  There are 23 sub-stem codes bringing the total number of leaf codes to 589.

Applying that number to the approximate leaf codes in the above table yields the following:

589/10,607 = 5.6%

589/14,622 = 4.0%

589/71,932 = 0.8%

Those numbers are consistent with the number of codes and diagnoses in psychiatry are certainly not excessive compared with the rest of medicine.     

 

References:

1:  Munk-Jørgensen P, Najarraq Lund M, Bertelsen A. Use of ICD-10 diagnoses in Danish psychiatric hospital-based services in 2001-2007. World Psychiatry. 2010 Oct;9(3):183-4. doi: 10.1002/j.2051-5545.2010.tb00307.x. PMID: 20975866; PMCID: PMC2948730. 

2:  Müssigbrodt H, Michels R, Malchow CP, Dilling H, Munk-Jørgensen P, Bertelsen A. Use of the ICD-10 classification in psychiatry: an international survey. Psychopathology. 2000 Mar-Apr;33(2):94-9. doi: 10.1159/000029127. PMID: 10705253.

3:  Leucht S, van Os J, Jäger M, Davis JM. Prioritization of Psychopathological Symptoms and Clinical Characterization in Psychiatric Diagnoses: A Narrative Review. JAMA Psychiatry. 2024;81(11):1149–1158. doi:10.1001/jamapsychiatry.2024.2652

4:  Mezzich JE, Fabrega H Jr, Coffman GA, Haley R. DSM-III disorders in a large sample of psychiatric patients: frequency and specificity of diagnoses. Am J Psychiatry. 1989 Feb;146(2):212-9. doi: 10.1176/ajp.146.2.212. PMID: 2783540.

5:  Chute CG, Çelik C. Overview of ICD-11 architecture and structure. BMC Med Inform Decis Mak. 2022 May 16;21(Suppl 6):378. doi: 10.1186/s12911-021-01539-1. PMID: 35578335; PMCID: PMC9109286.

6:  Harrison JE, Weber S, Jakob R, Chute CG. ICD-11: an international classification of diseases for the twenty-first century. BMC Med Inform Decis Mak. 2021 Nov 9;21(Suppl 6):206. doi: 10.1186/s12911-021-01534-6. PMID: 34753471; PMCID: PMC8577172.

7:  Quan H, Steinum O, Southern DA, Ghali WA. Coding mechanisms for main condition in ICD-11. BMC Med Inform Decis Mak. 2025 Jul 10;21(Suppl 6):387. doi: 10.1186/s12911-025-03069-6. PMID: 40640794; PMCID: PMC12243148.

What Does ERISA Say About AI Guardrails?

 

 

A colleague sent me a news article this morning about a couple suing a major AI firm for advice given by their chatbot to their son resulting in a fatal overdose.  As a psychiatrist most of what I read about problematic AI comes in the form of AI hallucinating false medical references (1), AI induced psychosis in people who either use it excessively or who are predisposed, or AI facilitating its own use by excessive praise or obsequiousness.  In the latter case it can result is emotional attachment to the AI that of course is unwarranted.  I have also flagged a couple of cases that illustrate the problems when AI is applied to moral and political decision making.

I decided to do a little more research on the subject.  I was surprised to find a Wikipedia page titled Deaths Linked To Chatbots. Thirty-three deaths are listed not including the case I was investigating. The suggested pathways to violence generally include overuse, emotional attachment, and bad advice biased toward reinforcing irrational decisions.  The evidence contained on this page highlights a couple of concepts that might not be apparent to most people including the architects of AI.  The first is the importance of emotion in human decision making. This was articulated by Bechara in the past who demonstrated that if there is a disruption between emotional and cognitive systems in the human brain – even basic decisions become impossible.  Other disruptions in the same systems can lead to an array of emotional dysregulation and the associated irrational and often socially inappropriate decisions.  Second, emotional biases clearly affect decision making in the case of intact brains.  There is perhaps no better example than the current American political system installing a less competent government that is clearly not in support of the wants and needs of most Americans.

Secondly, humans can form intense attachments to inanimate objects that are unable to reciprocate.  The classic example is developmentally normal transitional objects (stuffed animals, toys, blankets).  Winnicott theorized that in infancy – this object is recognized as not part of the self or external reality.  It is a fantasized relationship that represents a future “illusion”(2).  According to Winnicott’s theory the transitional object loses meaning during normal development and becomes irrelevant.  Persistence into later stages may indicate a normative transition like object attachment during grieving, to a way to compensate for the lack of interpersonal attachments, to personality or psychopathology. 

Chatbots can be significant attachment figures and this is currently an area of study (4-6).  The area of human – digital object transference is also being explored (6) as well as the projection of human needs onto a digital object (8), and more complex models of human-machine connectedness (9).  This literature is referenced primarily to indicate that there is a lot that is not known about the array of human responses to interactions with these machines and what the possibilities are.

Apart from my previous concerns that machines lack consciousness and have demonstrated a lack of adequate moral decision-making there is always the question of programming and algorithms. Both of the features are the bane of most Internet users who find that their most mundane interests are often amplified to result in a barrage of advertisements and sales offers.  And then there is the army of misinformation bots spreading foreign and national political propaganda 24 hours a day.  None of that requires AI but is there any doubt that AI will make it worse and harder to detect?

It is no secret that the current AI explosion is a multitrillion dollar enterprise being run by a handful of men who have shown no interest in the environment, social equity, or human rights. They immediately aligned themselves with an autocratic government at the highest levels and so far, have had no regulation of their AI.  As a result, that AI is spewing out massive amounts of information that the average citizen is taking as legitimate if not some type of advanced advice. The complications of that advice include the deaths, environmental damage from the required power generation, and societal damage from unemployment.  There is additional damage based on inequity from wealth concentration.  The barrage of pro-AI hype in the media greatly exceeds any realistic discussion of the downsides.  The only clear benefit that most people see is their ability to sit at home and entertain themselves with a chatbot or see if an AI can do their homework or other projects.  The purported efficiency seems offset by a tremendous amount of time wasted.

At the minimum – in the case that started this post there is a stark contrast between human decision makers and AI.  In 40 years of practice – I never recommended kratom by itself or with alprazolam (Xanax) or Benadryl (diphenhydramine).  In fact, I spent a considerable amount of time getting people off of alprazolam and later kratom. But I am not unique in this – I don’t know of any physician who would make these recommendations.  But those recommendations form the basis for the AI lawsuit. 

That highlights the danger of the current hype that AI will replace physicians or the predictable studies that comparing AI to physicians shows that AI can be safely consulted.  There are even stories that AI is prescribing drugs in some settings without physician input.  The question of agency is never addressed and that seems like the basis for this lawsuit.  Corporations always seem to do good job of avoiding responsibility in healthcare.  The classic example is the Employee Retirement Income Security Act of 1974 (ERISA).  The pre-emption clause of ERISA means that in employer-sponsored health plan covered employees cannot bring state malpractice or negligence claims against their managed care organization (MCO) for injuries from denial of plan benefits, utilization review decisions, failure to use qualified physicians, or improper plan administration.   The reviewing physicians working for MCOs are also generally protected and the associated arguments are that utilization review is not the practice of medicine and/or the reviewers have no accountability/duty to the patient. Several studies have documented the patient harms related to this accountability gap and despite several attempts at amelioration it remains largely intact and a considerable source of financial success for managed care organizations.

The critical question is whether this kind of accountability gap will exist with AI.  It is easy to envision a scenario where AI is implemented to review charts and prescribe low risk medications like many online services do now.  Will AI eventually take the place of physician reviewers employed by MCOs? Will consumers and patients be led to believe that AI is making decisions that affect their medical care based on the best available information or in the interest of the corporation. Current statistics suggest that there are tens of millions of these decisions made every year.  AI can greatly increase that as well as the harassment factor if decisions are being appealed.

With all of the political talk about guardrails for AI – it is important to recognize that these guardrails need to exist at several levels.  Right now, it is not much of a stretch to say that AI is out there practicing medicine without a license. In the majority of cases like the initial example, the user does not know if the search result if strictly from medical literature or something else.  The user does not know if the AI is exercising the judgment of an average physician or in malpractice parlance using the community standard of care.  The user does not know if their psychology in terms of defense mechanisms or attachment style to inanimate objects or AI is being exploited.  The user does not know if the AI is just telling them what they want to hear.  And the user does not know if the AI is providing information in their best interest or the interest of corporations or the government.

I read a study doing research for this post and subjects were asked to rate the professionalism of the AI.  In my opinion the single-most significant determinant of professionalism for physicians is accountability and duty to their patients.  It fuels not only the immediate encounter but the concept of life long learning and service to patients. It is usually evident over time but only indirectly in the form of positive results and a positive relationship over time.  AI in its current form does not have it and I am not convinced that a society or culture that came up with ERISA can construct physician-like guardrails around medical AI.   

 

George Dawson, MD, DFAPA

 

Supplementary 1:  It came to my attention after posting this that managed care organization (MCOs) have already implemented AI for utilization review and care denials.  Part of the problem in getting an accurate estimate of how much AI is involved is that this is an area where algorithms have been in place for a long time. Some of the care denials may be algorithmic and some may be due to a new AI interface.  This is what I have so far.  If you have additional references or data – please send it my way and I will add it to this post.

Automated prior authorizations is an early application for triage based on various data sources, medical necessity, and machine assistance on the provider side. A Congressional Investigation of the 3 major companies providing Medicare Advantage insurance plans showed that over the course of 4 years (2019-2022) – denials of prior authorization requests for post-acute care increased and was consistently larger than the denials for all other types of care (11 – see page 19 Figure 1). As United Health Care automated the process the denial rate increased.  The document is clear that prior authorization by these companies is highly profitable and even though a small percentage of denials are appealed – most of those appeals are also denied. The overriding concern is that AI or other automatic of the prior authorization process will greatly increase the number of denials overwhelming whoever is on the physician-patient side who needs to make the appeal.   It is more than a little ironic that a process that so clearly favors the managed care industry needs additional leverage from AI.

Disclaimer:  I have made the argument several times on this blog that prior authorization should just be made illegal since it serves no useful purpose other than making money for companies that do not actually provide patient care and it forces physicians and nurses to work for free while addressing these denials.  The total cost of that work was estimated to be worth $31B in 2009.  The estimated cost of drug utilization management alone is $93 billion (14).       

Supplementary 2:  In the past 5 years I have fielded many complaints about authorization for post-acute care (PAC) from friends, relatives, and people contacting me here to figure out what to do about it.  A typical scenario is a 70+ year old adult hospitalized for a a significant problem.  The hospital team wants them discharged ASAP of course even though in many cases their primary problem has not been adequately treated.  They realize the patient cannot care for themselves at home and there are often no caregivers available and want to transfer them to a skilled nursing facility (SNF) for rehabilitation.  In many cases it is specialized rehabilitation like post stroke, heart attack, or traumatic brain injury rehabilitation and the patient lacks basic skills to care for themselves.  I am fielding selected complaints but all of these transfers were denied - often repeatedly to the point the patient and family were demoralized and gave up.  In one case the patient was dead within 48 hours of discharge.  Given the results in reference 11 - this appears to be a financial strategy.       

References:

1:  Topaz M, Roguin N, Gupta P, Zhang Z, Peltonen LM. Fabricated citations: an audit across 2·5 million biomedical papers. Lancet. 2026 May 9;407(10541):1779-1781. doi: 10.1016/S0140-6736(26)00603-3. PMID: 42107362.

2:  Kernberg OF.  Object relations theories and techniques.  In:  Textbook of Psychoanalysis, 2^nd ed.  Person ES, Cooper AM, Gabbard GO, eds.   Washington DC: American Psychiatric Association Publishing, 2025: 57-75. 

3:  Bachar E, Canetti L, Galilee-Weisstub E, Kaplan-DeNour A, Shalev AY. Childhood vs. adolescence transitional object attachment, and its relation to mental health and parental bonding. Child Psychiatry Hum Dev. 1998 Spring;28(3):149-67. doi: 10.1023/a:1022881726177. PMID: 9540239.   

4:  Cheng N, Yu R. Measuring and understanding emotional attachment in human-AI relationships. Ergonomics. 2026 Feb 2:1-20. doi: 10.1080/00140139.2026.2622539. Epub ahead of print. PMID: 41622967. 

5:  Liu T, Lo TY, Wen KH, Sun Y, Wei ZQ. Pathways of long-term AI virtual companion app use on users' attachment emotions: a case study of Chinese users. Front Psychol. 2026 Jan 12;16:1687686. doi: 10.3389/fpsyg.2025.1687686. PMID: 41602682; PMCID: PMC12833267.

6:  Koles B, Nagy P. Digital object attachment. Curr Opin Psychol. 2021 Jun;39:60-65. doi: 10.1016/j.copsyc.2020.07.017. Epub 2020 Jul 22. PMID: 32823244.

7:  Holohan M, Fiske A. "Like I'm Talking to a Real Person": Exploring the Meaning of Transference for the Use and Design of AI-Based Applications in Psychotherapy. Front Psychol. 2021 Sep 27;12:720476. doi: 10.3389/fpsyg.2021.720476. PMID: 34646209; PMCID: PMC8502869.

8:  Saracini C, Cornejo-Plaza MI, Cippitani R. Techno-emotional projection in human-GenAI relationships: a psychological and ethical conceptual perspective. Front Psychol. 2025 Sep 29;16:1662206. doi: 10.3389/fpsyg.2025.1662206. PMID: 41089650; PMCID: PMC12515930.

9:  Boyd RL, Markowitz DM. Artificial Intelligence and the Psychology of Human Connection. Perspect Psychol Sci. 2026 Mar;21(2):192-220. doi: 10.1177/17456916251404394. Epub 2026 Jan 29. PMID: 41608879; PMCID: PMC12960742.

10:  Sahni NR, Carrus B. Artificial Intelligence in U.S. Health Care Delivery. N Engl J Med. 2023 Jul 27;389(4):348-358. doi: 10.1056/NEJMra2204673. PMID: 37494486.

11:  US Senate Permanent Subcommittee on Investigations. Refusal of Recovery: How Medicare Advantage Insurers Have Denied Patients Access to Post-Acute Care. October 17, 2024. Accessed March 24, 2025. hsgac.senate.gov/wp-content/uploads/2024.10.17-PSI-Majority-Staff-Report-on-Medicare-Advantage.pdf

12:  Mello MM, Trotsyuk AA, Mahamadou AJD, Char D. The AI Arms Race In Health Insurance Utilization Review: Promises Of Efficiency And Risks Of Supercharged Flaws. Health Aff (Millwood). 2026 Jan;45(1):6-13. doi: 10.1377/hlthaff.2025.00897. PMID: 41494115.

13: Casalino LP, Nicholson S, Gans DN, Hammons T, Morra D, Karrison T, Levinson W. What does it cost physician practices to interact with health insurance plans? Health Aff (Millwood). 2009 Jul-Aug;28(4):w533-43. doi: 10.1377/hlthaff.28.4.w533. Epub 2009 May 14. PMID: 19443477.

14: Butcher  L.  Can legislation save the day for challenges related to prior authorization?   Neurol Today. 2022;22(1):1-25. doi:10.1097/01.NT.0000817608.36002.47 

Why Psychodynamic Psychotherapy?

 

I am not averse to top ten lists and came up with 8-points initially but easily found another two.  I have had a few posts over the past year based on my experience in a psychotherapy seminar that I coteach with several experienced instructors.  We meet 2 hours a week – every week over the course of the year.  The first hour is a didactic based on the Cabaniss text Psychodynamic Psychotherapy: A clinical manual. (2).   Over the course of the year, we cover every bullet point and entering into this later than my colleagues – I have been impressed with the level of discussion from both the faculty and residents.  The second hour each week is dedicated to the discussion of specific cases.  Different perspectives are encouraged.  I have made note of when I got an idea for a post here from participation in that seminar.  This teaching format was carefully designed over the course of many years by the instructors who were there long before I joined.  They also describe it as a format where clear improvements can be observed in the residents practicing the techniques.  

Several issues come up when psychotherapy training is discussed for psychiatrists. Over the years it has been a hot political issue.  As previously noted – many people like to characterize the history of psychiatry in an oversimplified manner.  The original asylum psychiatrists had little more than moral treatment of Pinel and Tuke.  That was followed by a period of brain based descriptive asylum psychiatry focused on neuropathology and phenomenology.  That was followed by a period of psychoanalysis and psychodynamic psychiatry.  And finally biological psychiatry starting in about the mid-20^th century with advancements in somatic treatments.

That is the timeline that is typically used to describe American psychiatry – but things are always more complicated.  I trained in the 1980s when departments were often split between the psychotherapy staff and biological psychiatry staff – but the split was really an illusion.  The residents were trained in both. I trained with some of the top biological psychiatrists in the country and they also did psychodynamic formulations and psychotherapy.  During my 3 years of residency, I was supervised for an hour for every hour of psychotherapy I provided every week in addition to the training seminars in psychotherapy.   

At the national level experts in psychotherapy have always worked and published in parallel to the biological psychiatrists and neuroscience-based psychiatrists.  The reality is that you cannot practice psychiatry well without being able to integrate the medical, biological, and the psychotherapy dimensions of the field. 

A general psychotherapeutic approach to the patient is required in psychiatry for several reasons. All medical students learn diagnostic interviewing beginning in the first year of medical school. The basic principles are empathy and open-ended questions.  There is not much content about immediate problems in the interview, how to discuss difficult topics, how (or why) to expand on the phenomenology, and what to do about your personal reactions to the patient.  In a previous post I have discussed this as a reason why supportive psychotherapy is the clinical language of psychiatry.

The evaluation in psychiatry is very often an intervention point. It is not possible to interview the patient in a crisis and have them return at a later date to address the crisis.  Talking and psychotherapeutic interventions are the mainstay of crisis intervention. The psychiatrist needs to assist the patient in resolving the crisis.  These crises can happen at any point in time – even in patients who have been doing well for years.  A psychiatrist always needs to be prepared to do crisis intervention at any point in time and that involves good psychotherapeutic skills. 

Psychotherapy is the treatment of choice for many disorders.  It has been used for decades to treat severe personality disorders and the consensus lately is that it is the preferred treatment over medication in some of those scenarios.  The landscape can be confusing because branded and manualized therapies are often used in clinical trials and even though they can get equivalent results.  It is probably safe to say that psychodynamic psychotherapists used to shifting from supportive to interpretative modes see much of what they do in these psychotherapy manuals and trials.

The therapeutic alliance is most explicitly discussed in psychodynamic psychotherapy. It has been written about for decades and is important in all aspects of individualized psychiatric care.  In training it is also discussed as an important anchor point for clarifying the treatment process during periods of conflict or impasse.

Treatment setting is an important aspect of psychiatric care.  In acute care settings, the patient population is selected based on problem severity, lack of response to other therapies, need for additional modalities, and team-based care.  Transference and countertransference is complicated by the fact that it is now occurring at the team and institutional level.  Any psychiatrist working in that setting needs to be able to figure that out and intervene before there are any major problems and assist the team in managing reactions to specific patients and families.

Most of the suggested guidelines for psychotherapy training in residency are competency based rather than based on time.  The reality of training is that there is a shortage of staffing for any centrally recommended training program.  Programs are left to their own devices to provide training in this area. An untapped resource in many areas are retired psychiatrists and therapists who might be willing to volunteer to continue teaching.  That has been my role in this seminar and I find it highly rewarding. 

Training in psychodynamics and all of its theorists also provides and important historical context for the profession.  How were the original concepts modified over the years? Are some of these approaches and concepts (attachment theory, interpersonal psychotherapy, existential psychotherapy, infant psychotherapy, self-psychology, etc) still useful today? 

The most significant aspect of psychodynamic psychotherapy is the unique focus on consciousness. Unless you read about consciousness research explicitly this is the only place where that occurs.  I mentioned last week that when I attended medical school the emphasis was on objectivity and classifying people according to their disease and how well it could be characterized and whether those findings could be replicated. You do not have to be particularly bright to see the shortcoming of this approach.  No two people with asthma, brain tumors, or anemia are alike even after saying they have the same disease.  Their physical state, the way they think about it, their response to treatment, and the way they interact with you as their doctor is unique. In figuring out how to help them with whatever their psychiatric problem is – a diagnosis is only part of the story.  It requires thinking about who they are and why they might be reacting to things the way they do – a formulation.

Finally, and probably most importantly – psychodynamic training prepares the psychiatrist for what is ahead.  The first 5-10 years in practice is a potential minefield.  In today’s practice environment – psychiatrists are often overworked, seeing many people with severe medical and psychiatric problems who also have potentially severe interpersonal problems. Those problems are generally focused on aggression, sexuality, power dynamics like money and autonomy.  There is a general negativity about psychiatry in the press and on social media – largely from people promoting their own interests.  All of that can result in highly stressful situations in practice – especially if there are no colleagues around for consultation.  A psychiatrist can develop anxiety about some of these problems in practice and what can be done about them.  Although the issue has not been studied in the literature – my speculation is that psychiatrists trained in psychodynamics – especially if these topics have been explicitly discussed in training are more prepared to solve them in an effective way than psychiatrists without that exposure.  A typical way this issue is approached is to suggest rules that should not be violated.  Understanding what is happening at an emotional level is probably a better approach.

Reducing the mystery of psychotherapy and having a good idea of how it works is a clear goal. There have been times in my studies where the interventions seemed vague and which interventions were useful was not clear.  I can recall a few highlights over the course of my career that were very enlightening.  The first was a seminar with Otto Kernberg, MD about 30 years ago.  He provided the clearest definitions of the three basic intervention in psychoanalysis and psychodynamics that I have heard.  He described them as, clarification, confrontation and interpretation.  Clarification in this case is communicating the therapists empathic understanding of the patients experiences over time at the conscious and preconscious level.  Confrontation is not the usual adversarial sense, but is used to point out inconsistences or patterns in the patient’s narrative that may have escaped their attention.  In the seminar I attended, Kernberg pointed out that many people may be holding these inconsistencies outside of their awareness and the confrontation serves bring things together in a consistent narrative.  Finally, the interpretation connects patient's conscious experience and unconscious defenses, wishes, or conflicts.  Learning how to do that involves both reading about the theory and basic cases as well as seminar discussions with input from faculty and residents.   

Many consider the interpretation to be the mysterious part of psychodynamics and the secret handshake of this kind of psychotherapy.  It becomes more intuitive with a specific frame for psychodynamic therapy.  The two best examples I can think of are Interpersonal Psychotherapy and the Psychodynamic Life Narrative.  In the first case, Klerman and Weissman used psychodynamic principles to design a manualized therapy for depression.  It was subsequently applied to substance use disorders. Viederman designed the life narrative approach for crisis intervention in college students.  Both therapies are formulation based and designed to be used on a short-term basis.

The flexibility to go from an uncovering or transference based psychodynamic psychotherapy (TFP) to a supportive one is a critical skill.  There are people who will do better with supportive therapy and others who do well with TFP but in a crisis will do better with a supportive approach.  There is a broad range of flexibility in psychodynamic psychotherapy with supportive interventions that are identical to therapies taught separately as cognitive behavioral therapy, and behavioral therapy.  It also provides a framework for existential psychotherapy.     

Mechanisms of change in psychotherapy are written about broadly across therapies and specifically for psychodynamic psychotherapy.  It is safe to say that psychodynamic psychotherapy is more focused on the relationship with the therapist and the central role of emotions and insight. 

That is my brief commentary on the importance of psychodynamics and psychodynamic therapies in psychiatry.  There are many clinical trials for specific conditions that show psychodynamic therapy is effective. Some of those trials are 40 years old at this point.  We have many branded therapies advertised for specific conditions with features that overlap both supportive and psychodynamic psychotherapy adding further support to the claim it is a good approach to teaching psychiatrists.  When I look at the training recommendations for psychiatrists –  if you are running a training program and look at the three choices – psychodynamic psychotherapy should be preferred.  It covers two of the three types of therapy, has a rich history of involvement by psychiatrists at the clinical and theoretical level, and probably provides trainees with a better model for analyzing problems that occur in their future practices.

 

George Dawson, MD, DFAPA  

 

 

References:

 

1:  Kernberg OF.  Severe Personality Disorder: Psychotherapeutic Strategies.  Yale University Press; New Haven; 1984: 381 pp.  

2:  Klerman GL, Weissman MM, Rounsaville BJ, Chevron ES.  Interpersonal Psychotherapy of Depression.  Basic Books, Inc; New York; 1984: 255 pp.

3: Viederman M. The Psychodynamic Life Narrative. Psychiatry. 1983 Aug;46(3):236-246. PubMed PMID: 27719516.

4:  Holly A Swartz.  Interpersonal Psychotherapy (IPT) for depressed adults: Indications, theoretical foundation, general concepts, and efficacy. In: UpToDate, Roy-Byrne P (Ed), UpToDate, Waltham, MA, 2018.  Accessed February 17, 2018.

5:  Wampold BE. How important are the common factors in psychotherapy? An update. World Psychiatry. 2015 Oct;14(3):270-7. doi: 10.1002/wps.20238. PMID: 26407772; PMCID: PMC4592639.

6:  Høglend P, Hagtvet K. Change mechanisms in psychotherapy: Both improved insight and improved affective awareness are necessary. J Consult Clin Psychol. 2019 Apr;87(4):332-344. doi: 10.1037/ccp0000381. Epub 2019 Jan 10. PMID: 30628797.

7:  Churchill R, Moore THM, Davies P, Caldwell D, Jones H, Lewis G, Hunot V. Psychodynamic therapies versus other psychological therapies for depression. Cochrane Database of Systematic Reviews 2010, Issue 9. Art. No.: CD008706. DOI: 10.1002/14651858.CD008706. Accessed 12 May 2026.

8:  Høglend P. Exploration of the patient-therapist relationship in psychotherapy. Am J Psychiatry. 2014 Oct;171(10):1056-66. doi: 10.1176/appi.ajp.2014.14010121. PMID: 25017093.

9:  Nakamura K, Iwakabe S, Heim N. Connecting in-session corrective emotional experiences with postsession therapeutic changes: A systematic case study. Psychotherapy (Chic). 2022 Mar;59(1):63-73. doi: 10.1037/pst0000369. Epub 2021 Jul 22. PMID: 34291996.

10:  Abbass AA, Kisely SR, Town JM, Leichsenring F, Driessen E, De Maat S, Gerber A, Dekker J, Rabung S, Rusalovska S, Crowe E. Short‐term psychodynamic psychotherapies for common mental disorders. Cochrane Database of Systematic Reviews 2014, Issue 7. Art. No.: CD004687. DOI: 10.1002/14651858.CD004687.pub4. Accessed 12 May 2026.

11:  Perry JC, Bond M. Change in defense mechanisms during long-term dynamic psychotherapy and five-year outcome. Am J Psychiatry. 2012 Sep;169(9):916-25. doi: 10.1176/appi.ajp.2012.11091403. PMID: 22885667.

12: Babl A, Grosse Holtforth M, Perry JC, Schneider N, Dommann E, Heer S, Stähli A, Aeschbacher N, Eggel M, Eggenberg J, Sonntag M, Berger T, Caspar F. Comparison and change of defense mechanisms over the course of psychotherapy in patients with depression or anxiety disorder: Evidence from a randomized controlled trial. J Affect Disord. 2019 Jun 1;252:212-220. doi: 10.1016/j.jad.2019.04.021. Epub 2019 Apr 8. PMID: 30986736.

13:   Yakeley J. Psychoanalysis in modern mental health practice. Lancet Psychiatry. 2018 May;5(5):443-450. doi: 10.1016/S2215-0366(18)30052-X. Epub 2018 Mar 21. PMID: 29574047.

14:  Blatt SJ, Behrends RS. Internalization, separation-individuation, and the nature of therapeutic action. Int J Psychoanal. 1987;68 ( Pt 2):279-97. PMID: 3583573.

Medical Reasoning vs. A Diagnostic Manual

 

I taught a course on medical decision making and how not to mistake a physical illness for a psychiatric disorder from about 1990 to 2002. The main theorists at the time were all internists – Stephen Pauker, Jerome Kassirer, Richard Kopelman, David Eddy, and Harold Sox.  I read their papers and attended their courses.  State-of-the-art in those days involved extensive differential diagnosis, Bayesian analysis, and an awareness of an extensive list of potential cognitive biases. I had been impressed with the need for pattern matching and pattern completion and incorporated all those elements into my course.  I eventually pared it down to about 9 sections in the lecture notes illustrated with case vignettes.

My original emphasis was to recognize that there are several considerations when assessing the medical aspects of psychiatric care.  The first is the medical stability of the patient.  Can they be cared for on a psychiatric unit or do their medical needs require medicine or in some cases surgery?  Do they need referral to a generalist of specialist?  This is more complicated than it sounds because the patient is there seeing a psychiatrist for what is supposed to be a psychiatric problem.  But that presentation is complicated by several factors including most patients have no primary care physician and no routine health care maintenance. Many will come into the emergency department concerned about a medical problem but get sent to psychiatry. In that situation, people still get all of the acute medical illnesses including heart attacks, strokes, asthma attacks, pulmonary emboli, seizures, pneumonia, meningitis, encephalitis, and acute cholecystitis to name a few.  Many exhibit non-specific behaviors like agitation, crying out, aggression, or unresponsiveness that can be due to either a psychiatric disorder or a medical problem.    

The second is a psychiatric presentation of a physical illness in a communicating patient. The classic presentations involve brain pathology that is infection, inflammatory, vascular, trauma, or neurodegenerative.  Systemic endocrinopathies and inflammatory disorders are a close second. 

Finally, there is the patient with a clear psychiatric disorder who has intercurrent illness that is or is not known.  Examples that I have seen many times include current or new onset diabetes mellitus, profound anemia usually secondary to an upper or lower GI bleed, dermatology conditions that have often been neglected, symptomatic nutritional deficiencies (B12, folate, D), sexually transmitted diseases, complications of substance use like cirrhosis, and various acute and chronic infectious diseases.

Given that large population with diverse medical and psychiatric problems as well as diverse presentations that can include denying any physical problems – I typically reviewed how the diagnoses occurred.  Pattern matching was the fastest.  The physician has seen a physical finding, lab, behavior, etc – many times before, knows what it is, diagnoses it and treats it.  A good example is a rash.  Dermatologists are rash experts and can correctly classify rashes and marginal cases much faster than primary care physicians (4).  The same is true for diabetic retinopathy and ophthalmologists (5).  Until you have seen a person with severe mania or catatonia, neuroleptic malignant syndrome, or serotonin syndrome it is less likely that you can diagnose the conditions by reading criteria in a book.  Patterns are important for all medical specialists.

On the other end of the spectrum is the contemplative side of diagnosis.  There are several possible diagnoses, and it takes additional data, thought, and reasoning to come to a final diagnosis. Every medical student does this in their initial internal medicine rotation.  There is encouragement to produce a list of many diagnoses that might account for the presentation – but even as the case is being recorded or presented that list rapidly narrows to the apparent diagnosis.

In psychiatry, it may take much more data and collateral information to make a specific diagnosis at the initial presentation.  First episode psychosis (FEP) is a case in point. It is very important to determine what the symptoms onset was like and whether there were any associated mood symptoms or substance use problems. The patient may not be able to describe the phenomenology and depending on the circumstances treatment may be initiated while the diagnostic process is ongoing.  Teaching about the diagnostic process, we would spend time discussing what that might look like combined with a recursive approach to the patient and an awareness of cognitive and emotional biases.  I provided several examples of non-psychiatric physicians making errors due to emotional biases.

Since my course, the literature on medical decision making has changed to some degree.  There is some literature that addresses expertise in general at both the level of cognitive psychology (1) and neurobiology (2).  The general approaches have been to analyze expertise and diagnostic reasoning from the perspective of typical domains (cognitive, perceptual, motor) or to look at a general model and how that has developed over the years.

A dual processing model (3) is generally considered the best current representation of clinical reasoning and decision making.  In this model, there is a fast automatic, heuristic, and unconscious system called Type 1 and a slower conscious, analytical, and effortful system called Type 2.  Additional properties are indicated in the following table.

Parameter	Type 1	Type 2
Speed	Fast, automatic, unconscious/preconscious, little effort	Slow, deliberate, analytical, varying degrees of effort
Control	Minimum control, similar to automatic associations in everyday life except more focused	Control over thought process and direction
Systems and Processing	Pattern recognition and completion, implicit learning, access to long term memory	Working memory and manipulation of data in working memory, planning and reasoning based on that data
Memory Systems	Long term memory	Short term and working memory
Localization	-Orbitofrontal cortex (OFC) -Basal ganglia (caudate, putamen) -Insula -Anterior cingulate cortex -Amygdala -Hippocampus	-Dorsolateral prefrontal cortex (DLPFC) -Left inferior frontal gyrus -Middle frontal gyrus -Inferior parietal lobule -Precuneus -Hippocampus

 A clinical example of Type 1 reasoning is when a trained clinician recognizes a classic presentation of a medical illness, diagnosis, or finding.  An example I frequently use is when one of my Infectious Disease attendings who was an expert in Streptococcal infections recognized characteristic rash from across the room on a patient we were consulted for a different problem.  He made the diagnosis within seconds and told us how it could be confirmed.  In studies of the process the orbitofrontal cortex and limbic connections are activated.  Training is a critical element, especially seeing a maximum number of patterns and their variations.  Although the characterization is that this is a fast and automatic process, there is some room for deliberation.  For example, recognizing or attempting to classify equivocal cases without classic presentations. 

Type 2 reasoning is considered more of the typical process of differential diagnosis.  The findings are compared, analyzed, and accepted or rejected based on additional data and clinical judgment. This process is thought to localize in dorsolateral prefrontal cortex (DLPFC) the home of the working memory where data can be maintained and analyzed.  The left inferior frontal gyrus contributes to rule-based reasoning and hypothesis testing.  A clinical example from my experience is the case of the agitated stuporous patient.  These cases require a great deal of caution because they are most likely to represent a serious or life-threatening illness.  It requires a clinician who knows how to examine patients with stupor or coma and rapidly makes sense of the history and findings. It is a problem that can rarely be solved by Type 1 reasoning alone due to a fairly non-specific presentation.  Some of the critical points for hypothesis testing will be signs of increased intracranial pressure, purposeful response to painful stimuli, eye movements, reflex and musculoskeletal exam abnormalities, signs of infection, and meningeal signs.

The interaction between Type 1 and Type 2 systems is not necessarily sequential but it can be with the Type 1 system matching patterns that lead to hypothesis generation.  There is some evidence that in most clinical situations most of the diagnoses occur with Type 1 reasoning.  Experts can operate at the level of Type 1 reasoning due to extensive experience.  There is not necessarily a hard separation based on the properties in the table. Some hypothesis testing can occur at both levels.  Both systems are commonly grounded in both the limbic system and the hippocampus.

The human brain is capable of parallel distributed processing of data or information.  This means that there are many processing areas in the brain that are interconnected and they can all be working at once.  The modern conceptualization is brain networks that are active processing areas connected by white matter tracts widely distributed through the brain.  

That brings me to my model of diagnostic reasoning (see lead graphic and click to enlarge).  It is based on the course I taught, neuroanatomy and neurology, and what I have observed clinically. When I was talking about pattern matching 20 years ago based on my observations and reading studies in dermatology, ophthalmology, radiology, and pathology – the term seemed to fade rapidly from the diagnostic reasoning literature.  It was revived somewhat by the more recent focus on AI and comparison of that modality to humans.

There was a lull in Bayesian analysis after the invention of computerized programs like Quick Medical Reference (QMR) and Iliad.  They were designed to facilitate medical diagnoses by providing an exhaustive list of findings and their probabilities. These were 20^th century personal computer programs and not AI.  A study of these and 2 additional programs suggests that the programs got 52-71% of 105 diagnostic cases correct with 19-37% being the mean portion of correct diagnoses (6). Despite those figures the programs provided an additional 2 diagnoses per case that experts considered as relevant.  The authors recommended that the programs be used only by physicians who could include the relevant and exclude the irrelevant information provided by the programs.  The programs were discontinued without further modification or updates.  

That is the 8-mile-high view.  I plan to do a deeper dive into the neuroanatomy and neurophysiology.  But the clear reality of the situation is the ability to make a psychiatric diagnosis resides in the brain of a psychiatrist and not a classification manual or a checklist.   Manuals and checklists are crude approximations of some of the cognitive features that psychiatric experts possess.  Like all experts – skill will vary based on practice, exposure, and interest because of the effects on these brain systems.  But we are well past the point of equating what a psychiatrist does to a crude manual.  A manual never saved or treated anyone.  Further – the diagnostic reasoning process emphasizes elements that are important for education and training. It seems that in the past decades there has been a preoccupation with evidence-based research rather than the evidence itself. It does not do the physician or patient any good to be in a situation where that physician is unable to communicate with a person who is in a critical state and has no idea how to assess that problem.  Rearranging diagnostic criteria in a manual for the ninth or tenth time does not get you there.   

 

George Dawson, MD, DFAPA

Supplementary 1:   Before anyone says the diagram is too complex - it is a general diagram for any human diagnostician.  The main modifications for physicians and psychiatrists are the interactive aspects that include empathic comments, formulations, and numerous verbal interventions that other diagnosticians may not need to use.  The specifics about how these memory systems interact are not known at this point - I will be researching that over the next several months.  I borrowed the superposition concept from quantum mechanics - even though there are no wave functions for memory.         

 References:

.

1:  Bilalić M.  The Neuroscience of Expertise.  Cambridge University Press. Cambridge, United Kingdom. 2017.

2:  Maguire EA, Gadian DG, Johnsrude IS, Good CD, Ashburner J,  Frackowiak RSJ, Frith CD. 2000. Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci USA 97:4398–4403.

3:  Norman GR, Monteiro SD, Sherbino J, Ilgen JS, Schmidt HG, Mamede S. The Causes of Errors in Clinical Reasoning: Cognitive Biases, Knowledge Deficits, and Dual Process Thinking. Acad Med. 2017 Jan;92(1):23-30. doi: 10.1097/ACM.0000000000001421. PMID: 27782919.

4:  Federman DG, Concato J, Kirsner RS. Comparison of dermatologic diagnoses by primary care practitioners and dermatologists. A review of the literature. Arch Fam Med. 1999 Mar-Apr;8(2):170-2. doi: 10.1001/archfami.8.2.170. PMID: 10101989

5:  Sussman EJ, Tsiaras WG, Soper KA. Diagnosis of Diabetic Eye Disease. JAMA. 1982;247(23):3231–3234. doi:10.1001/jama.1982.03320480047025

6:  Berner ES, Webster GD, Shugerman AA, Jackson JR, Algina J, Baker AL, Ball EV, Cobbs CG, Dennis VW, Frenkel EP, et al. Performance of four computer-based diagnostic systems. N Engl J Med. 1994 Jun 23;330(25):1792-6. doi: 10.1056/NEJM199406233302506. PMID: 8190157.