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Figure 1.
Relative Differences in 3 Complexity Markers, by Physician Type
Relative Differences in 3 Complexity Markers, by Physician Type

Error bars indicate 95% CIs. Relative differences in all 9 complexity markers (by physician type) can be found in eFigure 3 in the Supplement.

Figure 2.
Complexity Rankings by Physician Type
Complexity Rankings by Physician Type

Using results from the regressions, the specialties were uniformly ranked for each marker of complexity. The ranks then were summed across complexities giving an overall complexity rank. Ties were broken using the highest frequency of the highest available rank between tied specialties.

Table 1.  
Demographics and Clinical Characteristics by Physician Type
Demographics and Clinical Characteristics by Physician Type
Table 2.  
Complexity Outcomes by Physician Typea
Complexity Outcomes by Physician Typea
1.
Shippee  ND, Shah  ND, May  CR, Mair  FS, Montori  VM.  Cumulative complexity: a functional, patient-centered model of patient complexity can improve research and practice.  J Clin Epidemiol. 2012;65(10):1041-1051. doi:10.1016/j.jclinepi.2012.05.005PubMedGoogle ScholarCrossref
2.
Pratt  R, Hibberd  C, Cameron  IM, Maxwell  M.  The Patient Centered Assessment Method (PCAM): integrating the social dimensions of health into primary care.  J Comorb. 2015;5:110-119. doi:10.15256/joc.2015.5.35PubMedGoogle ScholarCrossref
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Mathauer  I, Wittenbecher  F.  Hospital payment systems based on diagnosis-related groups: experiences in low- and middle-income countries.  Bull World Health Organ. 2013;91(10):746-756A. doi:10.2471/BLT.12.115931PubMedGoogle ScholarCrossref
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Grant  RW, Ashburner  JM, Hong  CS, Chang  Y, Barry  MJ, Atlas  SJ.  Defining patient complexity from the primary care physician’s perspective: a cohort study.  Ann Intern Med. 2011;155(12):797-804. doi:10.7326/0003-4819-155-12-201112200-00001PubMedGoogle ScholarCrossref
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von Elm  E, Altman  DG, Egger  M, Pocock  SJ, Gøtzsche  PC, Vandenbroucke  JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.  Lancet. 2007;370(9596):1453-1457. doi:10.1016/S0140-6736(07)61602-XPubMedGoogle ScholarCrossref
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Thompson  S, James  M, Wiebe  N,  et al; Alberta Kidney Disease Network.  Cause of death in patients with reduced kidney function.  J Am Soc Nephrol. 2015;26(10):2504-2511. doi:10.1681/ASN.2014070714PubMedGoogle ScholarCrossref
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Tonelli  M, Muntner  P, Lloyd  A,  et al; Alberta Kidney Disease Network.  Risk of coronary events in people with chronic kidney disease compared with those with diabetes: a population-level cohort study.  Lancet. 2012;380(9844):807-814. doi:10.1016/S0140-6736(12)60572-8PubMedGoogle ScholarCrossref
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Alexander  RT, Hemmelgarn  BR, Wiebe  N,  et al; Alberta Kidney Disease Network.  Kidney stones and kidney function loss: a cohort study.  BMJ. 2012;345:e5287. doi:10.1136/bmj.e5287PubMedGoogle ScholarCrossref
12.
Hemmelgarn  BR, Clement  F, Manns  BJ,  et al.  Overview of the Alberta Kidney Disease Network.  BMC Nephrol. 2009;10:30. doi:10.1186/1471-2369-10-30PubMedGoogle ScholarCrossref
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Tonelli  M, Wiebe  N, Fortin  M,  et al; Alberta Kidney Disease Network.  Methods for identifying 30 chronic conditions: application to administrative data.  BMC Med Inform Decis Mak. 2015;15:31. doi:10.1186/s12911-015-0155-5PubMedGoogle ScholarCrossref
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Stevens  PE, Levin  A; Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members.  Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline.  Ann Intern Med. 2013;158(11):825-830. doi:10.7326/0003-4819-158-11-201306040-00007PubMedGoogle ScholarCrossref
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Safford  MM, Allison  JJ, Kiefe  CI.  Patient complexity: more than comorbidity. the vector model of complexity.  J Gen Intern Med. 2007;22(suppl 3):382-390. doi:10.1007/s11606-007-0307-0PubMedGoogle ScholarCrossref
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Maxwell  M, Hibberd  C, Pratt  R,  et al. Patient centered assessment method tool. https://tinyurl.com/ybq33nsd. Published February 2015. Accessed September 13, 2018.
18.
Bayliss  EA, Ellis  JL, Shoup  JA, Zeng  C, McQuillan  DB, Steiner  JF.  Association of patient-centered outcomes with patient-reported and ICD-9–based morbidity measures.  Ann Fam Med. 2012;10(2):126-133. doi:10.1370/afm.1364PubMedGoogle ScholarCrossref
19.
Chrischilles  E, Schneider  K, Wilwert  J,  et al.  Beyond comorbidity: expanding the definition and measurement of complexity among older adults using administrative claims data.  Med Care. 2014;52(suppl 3):S75-S84. doi:10.1097/MLR.0000000000000026PubMedGoogle ScholarCrossref
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Boehmer  KR, Abu Dabrh  AM, Gionfriddo  MR, Erwin  P, Montori  VM.  Does the chronic care model meet the emerging needs of people living with multimorbidity? a systematic review and thematic synthesis.  PLoS One. 2018;13(2):e0190852. doi:10.1371/journal.pone.0190852PubMedGoogle ScholarCrossref
21.
Peek  CJ, Baird  MA, Coleman  E.  Primary care for patient complexity, not only disease.  Fam Syst Health. 2009;27(4):287-302. doi:10.1037/a0018048PubMedGoogle ScholarCrossref
22.
Siddiqui  M, Joy  S, Elwell  D, Anderson  GF.  The National Commission on Physician Payment Reform: recalibrating fee-for-service and transitioning to fixed payment models.  J Gen Intern Med. 2014;29(5):700-702. doi:10.1007/s11606-014-2785-1PubMedGoogle ScholarCrossref
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Centers for Medicare & Medicaid Services. Medicare Program; Revisions to Payment Policies Under the Physician Fee Schedule and Other Revisions to Part B for CY 2018; Medicare Shared Savings Program Requirements; and Medicare Diabetes Prevention Program. Final Rule. https://www.federalregister.gov/documents/2017/11/15/2017-23953/medicare-program-revisions-to-payment-policies-under-the-physician-fee-schedule-and-other-revisions. Published November 15, 2017. Accessed April 9, 2018.
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Markovitz  AA, Ellimoottil  C, Sukul  D,  et al.  Risk adjustment may lessen penalties on hospitals treating complex cardiac patients under Medicare’s bundled payments.  Health Aff (Millwood). 2017;36(12):2165-2174. doi:10.1377/hlthaff.2017.0940PubMedGoogle ScholarCrossref
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Dzau  VJ, Kirch  DG, Nasca  TJ.  To care is human—collectively confronting the clinician-burnout crisis.  N Engl J Med. 2018;378(4):312-314. doi:10.1056/NEJMp1715127PubMedGoogle ScholarCrossref
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Zullig  LL, Whitson  HE, Hastings  SN,  et al.  A systematic review of conceptual frameworks of medical complexity and new model development.  J Gen Intern Med. 2016;31(3):329-337. doi:10.1007/s11606-015-3512-2PubMedGoogle ScholarCrossref
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    13 Comments for this article
    EXPAND ALL
    Medical complexity
    Frederick Rivara, MD, MPH | University of Washington
    It is like no surprise that there is wide variation of complexity in the patients for whom various specialties provide care. What are the implications of this for the types of teams that should be assembled to care for these patients and the variations in payment which will be necessary to provide that care?
    CONFLICT OF INTEREST: Editor in Chief, JAMA Network Open
    But Geriatrics...
    Jonathan Treml, MBBS | University Hospitals Birmingham, UK
    This study immediately renders itself of limited value by using methodology that excludes geriatric medicine, the specialty that specifically deals with complexity and multi-morbidity.
    CONFLICT OF INTEREST: None Reported
    Family Physician analysis...
    Matt Norman |
    Great article, very interesting! As a family doc though, I'm not entirely clear how our profession can be compared to specialists. In my practice for instance, I have a huge number of complex care patients, but obviously I refer many of them to specialists for ongoing care (however, I still remain the 'medical home' for them all). Does the article suggest then that if we compared my 1800 patient roster to a nephrologist's 1800 patient roster (do nephrologists generally have that many patients? I may have missed this, but was size of practice incorporated into the analysis?), the latTer would have more complex patients overall? I think I would agree with that analogy, but I'm not really sure if this comparison can actually be made in practice?
    CONFLICT OF INTEREST: None Reported
    READ MORE
    Fundamentally flawed
    William Gibson, MBChB MRCP(UK) | University of Alberta, Edmonton, Canada
    We read with interest Tonelli et al’s analysis of the complexity of patients seen by different sub-specialties of internal medicine [1]. Although an interesting and worthwhile endeavour, the paper’s method has a fundamental flaw which renders its conclusions invalid.
    It is widely acknowledged that multimorbidity [2], frailty [3], and polypharmacy [4-6] increase markedly with increasing age. These complex patients are the bread and butter of geriatric medicine, the subspecialty of internal medicine dedicated to dealing with medical and functional problems in older people and are commonly seen in both inpatient and outpatient settings by Geriatricians.
    The authors excluded geriatricians
    from their analysis on the grounds that these specialists are paid by salary and do not contribute to the datasets explored by the authors. This however is not true; the majority of geriatricians in Alberta who are trained in GIM (rather than care of the elderly physicians, who are family doctors) are part of an alternative relationship plan in which shadow billing is a core component of performance measurement, and one to which many of the specialists included in the analysis in Alberta contribute; thus these data would be available to the authors.
    The omission of the medical speciality which specialises in complexity, multimorbidity, and polypharmacy from the analysis means Tonelli’s conclusions must be read with a large caveat; it is as if a paper claimed that the most common cause of end-stage renal failure is glomerulonephritis, having excluded people with diabetes from the analysis.
    Given the high rates of death and nursing home placement of those patients seen by nephrologists and given the data on improved outcomes in older people from comprehensive geriatric assessment in both outpatient [7] and inpatient [8] setting, the fact that complex older patients with a primary care provider who is part of a Primary Care Network have reduced Emergency Department use and reduced length of stay in hospital [9], and of their own data suggesting that in those with CKD who are at high risk of hopsitalisation there are opportunities to improve outcomes and reduce cost by focusing on better community-based care for this population [10], and alternative conclusion from the data presented would be that these that these patients should be seen by geriatricians and not by multiple single organ specialists.

    William Gibson, Frances Carr, Adrian Wagg
    University of Alberta, Division of Geriatric Medicine.


    1. Tonelli, M., et al., JAMA Network Open, 2018. 1(7): p. e184852.
    2. Marengoni, A., et al., Ageing Res Rev, 2011. 10(4): p. 430-9.
    3. Rockwood, K. et al., J Gerontol A Biol Sci Med Sci, 2007. 62(7): p. 722-7.
    4. Banerjee, A., et al., Int J Emerg Med, 2011. 4(1): p. 22.
    5. Herr, M., et al., Pharmacoepidemiol Drug Saf, 2015. 24(6): p. 637-46.
    6. Hohl, C.M., et al., Ann Emerg Med, 2001. 38(6): p. 666-71.
    7. Beswick, A.D., et al., Lancet, 2008. 371(9614): p. 725-35.
    8. Ellis, G., et al., Cochrane Database Syst Rev, 2017. 9: p. CD006211.
    9. McAlister, F.A., et al. CMAJ, 2018. 190(10): p. E276-E284.
    10. Ronksley, P.E., et al., Clin J Am Soc Nephrol, 2016. 11(11): p. 2022-2031.
    CONFLICT OF INTEREST: None Reported
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    Deriving the wrong answer by measuring the wrong thing
    Lee Green, MD MPH | University of Alberta and University of Michigan
    This article seems to conflict with work by other authors (1,2,3) that show internal medicine and family medicine to have substantially more complex visits than subspecialists. However, this article is technically correct; it is unfortunately also misleading. The difference between this article and previous literature is that this article measures patient complexity, and other studies measured visit complexity.

    Patient complexity and visit complexity must not be conflated. Particularly in Canada, where patients usually require a referral from their family physician to see a specialist, it is inevitable that specialists have many complex patients. However, as previous research has shown,
    they address fewer problems per visit than generalist physicians. That reflects specialists doing their jobs: addressing the single problem germane to their role, and perhaps one or two closely related.

    The family physician or general internist however must address all the patient's health issues, and hence research on the complexity of visits (vs. patients) gives a very different picture than this paper. Further, the general internist or family physician must not only address each problem, but address the integration of them – a task which increases in complexity exponentially, not linearly, with the number of conditions addressed.

    The generalist's task remains the most complex in modern medicine.

    1. Katerndahl D, Wood R, Jaen CR. Family Medicine Outpatient Encounters are More Complex Than Those of Cardiology and Psychiatry. The Journal of the American Board of Family Medicine 2011 Jan 1;24(1):6–15.
    2. Katerndahl D, Wood R, Jaén CR. Complexity of ambulatory care across disciplines. Healthcare [Internet]. 2015 Feb [cited 2015 Mar 8]; Available from: http://linkinghub.elsevier.com/retrieve/pii/S2213076415000184
    3. Moore M, Gibbons C, Cheng N, Coffman M, Petterson S, Bazemore A. Complexity of ambulatory care visits of patients with diabetes as reflected by diagnoses per visit. Primary Care Diabetes 2016;10(4):281–6.
    CONFLICT OF INTEREST: None Reported
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    Who Actually Integrates Care for Complex Patients?
    Kurt Stange, MD, PhD | Case Western Reserve University
    This study provides an interesting high level analysis that requires a more on-the-ground perspective to correctly interpret the policy implications.

    The study’s complexity measure provides markers of downstream consequences of medical and social complexity, but does not capture the degree to which assessed physicians actually deal with that complexity.

    The measure of complexity is blind to the integrating, prioritizing and prioritizing functions at the heart of a generalist approach.1,2 The broad scope of family medicine involves integrating care across multiple chronic illnesses, acute complaints, mental health, prevention, family, and social care. Even an apparently simple
    diabetes follow up visit to family physicians involves managing a median of 25 problems.3 The Tonelli study measure does not assess the degree to which the complexity is addressed ― usual in family medicine ― or whether complexity is considered primarily as context for a narrower focus on a specific disease, as expected in specialty care. The integrating function of family medicine often involves reducing the number of overlapping medications prescribed by multiple specialists, giving family medicine the appearance of lower complexity in the Tonelli measure. Coordination of care by family medicine means that patients with greater complexity are selectively included in the specialist sample, even if the specialists deal with only a narrow spectrum of that complexity. Further, research demonstrating that strong primary care is associated with reduced emergency department and hospital visits4 means that successful management of complexity by family physicians would make their patients look less complex by the measure used in this study.

    Research,1,2,5-7 based on who actually is managing patient complexity, comes to nearly opposite conclusions from the Tonelli et al study.8

    It is important that policymakers not take away from this study a simplistic view that family medicine lacks complexity. Integrating, personalizing and prioritizing care for whole people and families is among the most complex functions in health care. We agree with the authors that managing complexity takes time. The management of complexity is the centerpiece of the generalist approach in family medicine1,2 and deserves to be recognized, and supported with resources, the most precious of which is time with the patient.

    Kurt Stange, MD, PhD
    David Katerndahl, MD, MA
    Rebecca Etz, PhD

    1. Stange KC. The generalist approach. Ann Fam Med. 2009;7:198-203.
    2. Heath I, Rubenstein A, Stange KC, van Driel M. Quality in primary health care: a multidimensional approach to complexity. BMJ. 2009;338:b1242.
    3. Bolen SD, Sage P, Perzynski AT, Stange KC. No moment wasted: the primary-care visit for adults with diabetes and low socio-economic status. Primary Health Care Research & Development. 2016;17(1):18-32.
    4. Bazemore A, Petterson S, Peterson LE, Bruno R, Chung Y, Phillips RL. Higher Primary Care Physician Continuity is Associated With Lower Costs and Hospitalizations. Ann Fam Med. 2018;16(6):492-497.
    5. Bolen SD, Stange KC. Investing in relationships and teams to support managing complexity. J Gen Intern Med. 2017;32(3):241-242.
    6. Peek CJ, Baird MA, Coleman E. Primary care for patient complexity, not only disease. Fam Syst Health. 2009;27:287-302.
    7. Sturmberg JP. Systems and complexity thinking in general practice: part 1-clinical application. Aust Fam Physician. 2007;36:170-173.
    8. Katerndahl D, Wood R, Jaén CR. Complexity of ambulatory care across disciplines. Healthc. 2015;3(2):89-96.
    CONFLICT OF INTEREST: None Reported
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    To compare research results, work from a shared definition of the subject matter
    Charles Peek, PhD | Dept of Family Medicine and Community Health, University of MN Medical School
    This study 1 addresses a subject important to clinicians, patients, and systems. But its conclusions about the complexity of work across physician disciplines, including primary care, is inconsistent with research on how complexity is actually managed. 2,3 This calls for conversation in the field to understand how different authors can reach such different conclusions. It is more important for clinicians of all stripes to provide clinical and policy direction for complex patients than try to determine who has the most complex work.

    Such conversation could aim to 1) reach a definition of patient complexity good enough and
    shared enough to do comparable research, and 2) overcome the challenges in doing such research without oversimplifying the definition.

    The authors open with a high-level definition consistent with others in the field, including medical, social, economic, situational, health literacy, coordination and health system organization as dimensions of complexity 4,5. But the study in effect truncated that definition when it relied chiefly on administrative data on medical complexity. The authors carefully acknowledged that their analysis would have been strengthened by other data, giving specific examples.

    Focusing chiefly on administrative data is understandable because data on social factors, distress, coordination, and system characteristics are not well defined or easily retrieved in health records. A challenge for us is to go beyond currently accessible administrative data. Otherwise studies can’t capture what we mean by complexity or who is managing what part of it. Authors using different aspects of a general definition are likely to reach different conclusions.

    Continued conversation is also needed to 1) distinguish complex clinical presentations from downstream effects; and 2) distinguish patient complexity from care complexity. A patient-centered health system will respond in a non-judgmental way to patients with complex presentations. And it will respond in a self-challenging way to a mismatch between how the care system is set up and what it faces daily by way of complex presentations. As clinicians of all stripes, we can help our shared patients most by responding in these ways rather than by comparing who has the most complex work to do.

    References
    1. Tonelli M, Wiebe N, Manns BJ, Klarenbach SW, James MT, Ravani P, Pannu N, Himmelfarb J, Hemmelgarn BR. Comparison of the complexity of patients seen by different medical subspecialists in a universal health care system. JAMA Network Open. 2018;1(7):e184852.
    2. Katerndahl D, Wood R, Jaen CR. Family Medicine Outpatient Encounters are More Complex Than Those of Cardiology and Psychiatry. The Journal of the American Board of Family Medicine 2011 Jan 1;24(1):6–15.
    3. Stange KC, Cherng ST, Riolo RL, Homa L, Rose J, Hovmand PS, Kraus A. No longer looking just under the lamp post: modeling the complexity of (primary) health care. In: Kaplan G, Diez-Roux A, Galeo S, Simon C, eds. Growing Inequality: Bridging Complex Systems, Population Health, and Health Disparities. Washington, D.C., Westphalia Press, 2017. pp 81-107.
    4. Peek CJ, Baird MA, Coleman E. Primary care for patient complexity, not only disease. FamSystHealth.2009; 27(4):287-302. doi:10.1037/a0018048
    5. Pratt R, Hibberd C, Cameron IM, Maxwell M. The Patient Centered Assessment Method (PCAM): Integrating the social dimensions of health into primary care. J Comorb. 2015;5:110-119. doi:10.15256/joc.2015.5.35.
    CONFLICT OF INTEREST: None Reported
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    Primary vs specialty are
    Armand Rodriguez | Holy Cross Hospital
    Dr Lee Green’s comment is spot on. It’s the visit complexity, NOT the patient complexity. Did these authors miss the point that sub specialist address mainly one problem while we primary care physicians address most?
    Probably a well -intentioned but ultimately misguided study question.
    CONFLICT OF INTEREST: None Reported
    Age Adjustment Might Tell a Different Story
    Kevin Savage, BA | Beth Israel Deaconess Medical Center
    We read with interest, "Comparison of the Complexity of Patients Seen by Different Medical Subspecialists in a Universal Health Care System" in which the authors observed the highest levels of patient medical complexity among nephrologists, infectious disease physicians, and neurologists(1). The lowest complexity was observed among allergists, dermatologists, and family medicine physicians. Two methodological issues arise in interpreting these results: the age of the patient population and expected comorbidities in the relevant population. Many dermatologists, allergists and family physicians see children. Indeed, there was a vast difference in the ages of patients seen by different specialists, with >50% of nephrology patients over the age of 60, whereas allergy, dermatology and family physicians had 88.4%, 64.3% and 75.2% of their patients under the age of 60, respectively. This difference surely has an impact on the variables used to calculate complexity, especially: number of comorbidities, days hospitalized, emergency department visits, likelihood of long-term care placement, and risk of mortality. Second, and further confounding this problem is that many of the comorbidities studied were diseases of older patients, such as malignancies, hypertension, Parkinson disease, stroke, myocardial infraction, et cetera. The adult patients these physicians care for may well be equivalent in their complexity.

    In conclusion, the results of this study are difficult to interpret; adjustments for ages and age appropriate comorbidities might tell a different story.

    Kevin T. Savage, BA, 1
    Kelsey S. Flood, MD, 2
    Alexa B. Kimball, MD, MPH, 2

    1- Drexel University College of Medicine, Philadelphia, PA, 19129
    2- Clinical Laboratory for Epidemiology and Applied Research in Skin, Beth Israel Deaconess Medical Center, Boston, MA, 02215

    References:
    1. Tonelli, M. et al. Comparison of the Complexity of Patients Seen by Different Medical Subspecialists in a Universal Health Care System. JAMA Netw. Open 1, e184852 (2018).
    CONFLICT OF INTEREST: None Reported
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    An exclusion
    Katherine Prather, MD | University of Missouri
    An unfortunate exclusion in the analysis is obstetrics and gynecology. It is a surgical supspeciality to be sure but also a very medical one. I’d liked to have seen where our patient complexity fell.
    CONFLICT OF INTEREST: None Reported
    Family physicians provide care for everyone
    Braden O'Neill, MD, DPhil, CCFP | Department of Family and Community Medicine, University of Toronto and North York General Hospital
    The authors attempted to explore a challenging area and acknowledged that their definition of complexity is one of many. Several key issues, such as their lack of distinction between patient complexity and visit complexity (see Green's comment above) have been raised in previous responses. A further issue is that focusing on the 'mean' number of comorbidities as the authors have done, does not produce valid comparisons between the complexity of patients of family physicians and patients of medical specialists.

    Medical specialists see a smaller proportion of patients and are almost always involved in care on a referral basis, sent
    from a family physician (or through an emergency department). Most patients receive all or almost all of their care through family physicians and other primary care providers in community settings.

    In this study, 99% of patients saw a family physician during the study period, whereas only 44% of patients saw any medical specialist. While it is true (and appropriate) that some medical specialties see more complex patients ‘on average’, the authors failed to acknowledge that almost all of these patients are also receiving care from family physicians in addition to seeing specialists. Their approach to assessing the complexity of patients seen by family physicians therefore merely reflects the prevalence of these conditions in the population.

    This may be a relevant comparison between specific medical specialties but I do not believe it is an appropriate one in the case of family medicine. Other studies involving family medicine and complexity have looked at complexity by unit time and complexity by volume, and both have shown consistently that family physicians manage more complexity in less time than other specialties.
    CONFLICT OF INTEREST: None Reported
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    Response from the authors (1 of 2)
    Marcello Tonelli, MD SM MSc | University of Calgary
    We thank all those who commented for their thoughtful responses. We agree with most of the sentiments expressed thus far. Below we present our own reflections in response.

    First, if we were writing this article again, we would represent the specialties as “Specialty A,” “Specialty B,” etc rather than identifying them by name. Although this alternative approach would undoubtedly have reduced interest in our article, it might have defused some of the hostility that we have experienced on social media while clarifying our key point – “The relative rank of the different specialties studied is less important than the
    finding that there are wide variations in complexity between specialties, which has implications for medical education and health policy.” As Dr. Rivara says, this finding of between-specialty differences in complexity is not especially surprising, even if it has been rarely demonstrated. Why then are many health systems structured and resourced as though these differences do not exist?

    Second, our analytical approach required a referent category to which the various medical specialties were compared. In the Alberta health system, family medicine was the logical referent category. The relatively low complexity of the many healthy patients seen in family medicine practices is responsible for the low complexity score of the “average” family medicine patient. This does not mean that family medicine practice is devoid of complex patients (or complex visits) and certainly was not intended as a criticism of family physicians. We agree with Drs. Norman, Stange, and O’Neill’s comments about the challenges of family medicine practice and the implications of these challenges for practitioners and policy.

    Third, we thoroughly agree with Drs. Peek and Stange that more nuanced data will be required to assess how complexity is currently managed, and by whom. We agree that without such data, it will be difficult to provide specific policy solutions and to provide truly patient-centered care. We hope that the simpler data presented in our study will help to inform these future studies.

    Fourth, we agree with Dr. Savage that age-adjustment might lead to different results. However, age-adjusted analyses were not directly relevant to our goal, which is why we did not report them. As stated in our article: “Because the emphasis of this article was to capture the actual complexity of patients seen by the different physician types (rather than to examine the factors responsible for any observed differences, or to test for an independent association of complexity with physician group), we did not do adjusted analyses.” Presenting age-adjusted analyses would help to explain the findings (e.g. patients seen by neurologists tend to be older than those seen by allergists) but would not change the fact that the average neurology patient appears to be more complex that the average allergy/immunology patient.

    (continued)
    CONFLICT OF INTEREST: I am an author of the article and a nephrologist.
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    Response from the authors (2 of 2)
    Marcello Tonelli, MD SM MSc | University of Calgary
    (continued)

    Fifth, Dr. Gibson and Treml appear to misunderstand our findings, as we do not believe that including geriatricians in our analysis would have changed our conclusion. Further, Dr. Gibson is incorrect in saying that data on the complexity of patients seen by Alberta geriatricians were available to us. Although Dr. Gibson indicates that “shadow billing is a core component of performance measurement”, we found only 456 claims submitted by geriatricians during the study period, in contrast to 391,462 for cardiologists. Possible explanations for the low observed claim volume among geriatricians include (1) a lower number of patients seen
    each day due to higher patient complexity, (2) some claims by geriatricians being misclassified as belonging to other specialties, and (3) infrequent submission of claims by geriatricians to the shadow billing process, due to lack of financial incentive. Although all three explanations probably play a role, as stated in our article we favor the latter. Regardless of the explanation, although we had intended to include geriatricians in our article, we were unable to do so as we did not have the necessary data.

    Finally, we thank Dr. Jim Dickinson for pointing out errors in our article. We apologize for these errors, which were introduced when we reformatted the manuscript for resubmission following peer review. These errors do not affect the conclusions and no changes are required to the key points, abstract, introduction, methods, discussion or conclusions sections. A correction notice (erratum) is forthcoming.

    Marcello Tonelli MD SM MSc for the authors
    CONFLICT OF INTEREST: I am an author of the article and a nephrologist.
    READ MORE
    Original Investigation
    Health Policy
    November 30, 2018

    Comparison of the Complexity of Patients Seen by Different Medical Subspecialists in a Universal Health Care System

    Author Affiliations
    • 1Department of Medicine, University of Calgary, Calgary, Alberta, Canada
    • 2Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
    • 3Department of Medicine, University of Washington, Seattle
    JAMA Netw Open. 2018;1(7):e184852. doi:10.1001/jamanetworkopen.2018.4852
    Key Points español 中文 (chinese)

    Question  Are there differences in the complexity of patients seen by different types of physicians?

    Findings  In this population-based cohort study of 2.5 million Canadian adults, there were substantial differences in markers of complexity for patients seen by different types of physicians, including medical subspecialists. Patients seen by nephrologists, infectious disease specialists, and neurologists were consistently more complex, whereas patients seen by allergists, dermatologists, and family physicians consistently tended to be less complex.

    Meaning  Substantial between-specialty differences were found in 9 different markers of patient complexity. The relative rank of the different specialties studied is less important than the finding that there are wide variations in complexity between specialties, which has implications for medical education and health policy.

    Abstract

    Importance  Clinical experience suggests that there are substantial differences in patient complexity across medical specialties, but empirical data are lacking.

    Objective  To compare the complexity of patients seen by different types of physician in a universal health care system.

    Design, Setting, and Participants  Population-based retrospective cohort study of 2 597 127 residents of the Canadian province of Alberta aged 18 years and older with at least 1 physician visit between April 1, 2014 and March 31, 2015. Data were analyzed in September 2018.

    Exposures  Type of physician seeing each patient (family physician, general internist, or 11 types of medical subspecialist) assessed as non–mutually exclusive categories.

    Main Outcomes and Measures  Nine markers of patient complexity (number of comorbidities, presence of mental illness, number of types of physicians involved in each patient’s care, number of physicians involved in each patient’s care, number of prescribed medications, number of emergency department visits, rate of death, rate of hospitalization, rate of placement in a long-term care facility).

    Results  Among the 2 597 127 participants, the median (interquartile range) age was 46 (32-59) years and 54.1% were female. Over 1 year of follow-up, 21 792 patients (0.8%) died, the median (range) number of days spent in the hospital was 0 (0-365), 8.1% of patients had at least 1 hospitalization, and the median (interquartile range) number of prescribed medications was 3 (1-7). When the complexity markers were considered individually, patients seen by nephrologists had the highest mean number of comorbidities (4.2; 95% CI, 4.2-4.3 vs [lowest] 1.1; 95% CI, 1.0-1.1), highest mean number of prescribed medications (14.2; 95% CI, 14.2-14.3 vs [lowest] 4.9; 95% CI, 4.9-4.9), highest rate of death (6.6%; 95% CI, 6.3%-6.9% vs [lowest] 0.1%; 95% CI, <0.1%-0.2%), and highest rate of placement in a long-term care facility (2.0%; 95% CI, 1.8%-2.2% vs [lowest] <0.1%; 95% CI, <0.1%-0.1%). Patients seen by infectious disease specialists had the highest complexity as assessed by the other 5 markers: rate of a mental health condition (29%; 95% CI, 28%-29% vs [lowest] 14%; 95% CI, 14%-14%), mean number of physician types (5.5; 95% CI, 5.5-5.6 vs [lowest] 2.1; 95% CI, 2.1-2.1), mean number of physicians (13.0; 95% CI, 12.9-13.1 vs [lowest] 3.8; 95% CI, 3.8-3.8), mean days in hospital (15.0; 95% CI, 14.9-15.0 vs [lowest] 0.4; 95% CI, 0.4-0.4), and mean emergency department visits (2.6; 95% CI, 2.6-2.6 vs [lowest] 0.5; 95% CI, 0.5-0.5). When types of physician were ranked according to patient complexity across all 9 markers, the order from most to least complex was nephrologist, infectious disease specialist, neurologist, respirologist, hematologist, rheumatologist, gastroenterologist, cardiologist, general internist, endocrinologist, allergist/immunologist, dermatologist, and family physician.

    Conclusion and Relevance  Substantial differences were found in 9 different markers of patient complexity across different types of physician, including medical subspecialists, general internists, and family physicians. These findings have implications for medical education and health policy.

    Introduction

    Patient complexity can be defined as an interaction between the “personal, social, and clinical aspects of the patient’s experience”1 that complicates patient care. For example, increasing age and comorbidity, social factors (eg, poverty and lower level of education), treatment characteristics (eg, number of medications), and contextual factors (eg, residence in long-term care) all influence perceived patient complexity2—and the prevalence of complexity appears to be increasing in health systems worldwide. There is general agreement that patient complexity increases the time and resources required to provide optimal care. However, payments to health care facilities and physicians are both frequently based on patient volume rather than patient complexity.3-5 Even in systems that are not fee-for-service based, the time allotted to see a given number of patients often does not account for patient complexity.6

    Clinical experience suggests that the complexity of patients varies substantially between different medical specialties, although empirical data are lacking. To better understand the complexity of patients receiving care from different types of physicians, enabling a better estimation of the likely resource needs of these clinical populations, we compared the complexity of patients seen by different types of physician in a universal health care system. Since there is no consensus of how complexity should be measured,7 we used the number of comorbidities, the presence of mental illness, the number of types of physicians involved in each patient’s care, the number of physicians involved in each patient’s care, the number of prescribed medications, the number of emergency department visits, and the rate of adverse clinical outcomes (death, all-cause hospitalization, and placement in a long-term care facility) as proxies for complexity. We hypothesized that we would observe substantial differences in these measures of complexity across patients seen by the different types of physician in our study.

    Methods

    This retrospective population-based cohort study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.8 The institutional review boards at the University of Alberta and the University of Calgary approved this study and waived the requirement for participants to provide consent.

    Data Sources and Cohort

    We used a previously described database9-11 that incorporates data from Alberta Health (the provincial health ministry), including physician claims, hospitalizations, ambulatory care utilization, and Alberta pharmaceutical network data; the database also collects information from the clinical laboratories in Alberta, Canada. This database has population-based coverage of a geographically defined area, including demographic characteristics, health services utilization, and clinical outcomes. Indigenous status includes people who are registered as First Nations or recognized as Inuit. Additional information on the database is available elsewhere, including the validation of selected data elements and the standardization and calibration of serum creatinine assays.12 All individuals registered with Alberta Health were included in the database (all Alberta residents are eligible for insurance coverage by Alberta Health and >99% participate in coverage). The database was used to assemble a cohort of adults (aged ≥18 years) who resided in Alberta on April 1, 2014. Patients’ residential postal codes were used to classify them as residing in a rural area13 or in a lower-income neighborhood using the Statistics Canada definition of lowest neighborhood income quintile.13 We followed patients from April 1, 2014 (baseline), until death, out-migration from Alberta, or study end (March 31, 2015), whichever was earliest.

    Comorbidities

    Comorbidities were defined using a previously published framework with 29 validated algorithms as applied to Canadian physician claims data, each of which had positive predictive values of 70% or greater as compared with a gold-standard measure such as medical record review.14 These comorbidities were alcohol misuse, asthma, atrial fibrillation, lymphoma, nonmetastatic cancer (breast, cervical, colorectal, pulmonary, and prostate cancer), metastatic cancer, chronic heart failure, chronic pain, chronic obstructive pulmonary disease, chronic hepatitis B, cirrhosis, severe constipation, dementia, depression, diabetes, epilepsy, hypertension, hypothyroidism, inflammatory bowel disease, irritable bowel syndrome, multiple sclerosis, myocardial infarction, Parkinson disease, peptic ulcer disease, peripheral vascular disease, psoriasis, rheumatoid arthritis, schizophrenia, and stroke or transient ischemic attack. Each patient was classified with respect to the presence or absence of these 29 chronic conditions at baseline.15 Detailed methods for classifying comorbidity status and the specific algorithms used are found elsewhere.14 The presence of chronic kidney disease was also ascertained, captured using the single closest outpatient measurement of creatinine and albuminuria within 1 year of baseline, and defined based on international guidelines.15

    Physician Care

    We used outpatient and inpatient physician claims data to determine the physician or physicians who saw each patient. A single claim from a given physician for an individual patient in the year prior to baseline was sufficient to define the former as being seen by the latter. We focused on physicians whose practices are nonsurgical, including family physicians, general internists, and medical subspecialists. Medical subspecialists were defined as physicians with qualifications in cardiology, clinical immunology and allergy, dermatology, endocrinology, gastroenterology, hematology, infectious diseases, nephrology, neurology, rheumatology, or respiratory medicine. In all analyses, we excluded patients who did not receive care from any of these physicians in the year prior to baseline. Medical oncologists and specialists in geriatric medicine were excluded because in Alberta, these physicians are predominantly paid by salary and do not submit claims for most of their clinical encounters. Groups were not mutually exclusive, meaning that a patient who was seen by a family physician, a cardiologist, and a clinical allergist/immunologist would be classified as being seen by all of these physicians.

    Markers of Complexity

    We considered 9 markers as proxies for patient complexity. Seven were measured in the year prior to follow-up to minimize the impact of the competing risk of mortality on nondeath outcomes: the number of comorbidities, the number of uniquely prescribed medications (defined by unique chemical entities as assessed by prescriptions filled), the presence of a mental health condition (defined by alcohol misuse, depression, or schizophrenia), the number of physician types seen by each patient, the total number of physicians involved in each patient’s care, the number of days spent in a hospital, and the number of emergency department visits. The remaining 2 markers, the risk of new placement into long-term care and the risk of all-cause death, were measured during the year of follow-up.

    For analyses using physician type as an outcome, we considered the medical subspecialties listed in the Physician Care section, general internists and family physicians, and all other physicians who submit claims for patient visits and procedures. Nonphysician health professionals, such as chiropractors, dentists, and dieticians, were not included.

    Statistical Analysis

    We did analyses with Stata MP statistical software version 15.0 (StataCorp) and reported baseline descriptive statistics as counts and percentages. Probabilities and means were reported where appropriate. Confidence intervals for probabilities and means were calculated using exact binomial and exact Poisson methods. We used unadjusted logistic regression to determine the associations between scenarios of physician care and the ratio of odds for dichotomous outcomes and unadjusted Poisson regression to determine the associations between scenarios of physician care and the ratio of means for count outcomes. Between-group variability (physician groups) was measured using χ2 tests of equality between model coefficient estimates. The threshold for statistical significance was set at 2-sided P < .05. Because the emphasis of this article was to capture the actual complexity of patients seen by the different physician types (rather than to examine the factors responsible for any observed differences, or to test for an independent association of complexity with physician group), we did not do adjusted analyses. Using results from the regressions, the specialties were uniformly ranked for each complexity marker, with the highest ratio (rate ratio or odds ratio) receiving the highest rank. The ranks were then summed across the 9 complexity markers giving an overall complexity rank for each physician type. In sensitivity analyses, we considered the patient-visit (1 claim) as the unit of analysis rather than a patient, meaning that patients who were seen more frequently were given more weight. In further sensitivity analyses, we required at least 2 claims (on ≥2 days), or at least 3 claims (on ≥3 days) to be sufficient for a given physician to have seen an individual patient (in the year prior to baseline). We also considered the 1-year cohort beginning in April 1, 2009.

    Results
    Characteristics of Study Patients

    Patient flow is shown in eFigure 1 in the Supplement. Overall 1 039 403 patients (28.6%) were excluded because they were not seen by at least 1 family physician, general internist, or medical subspecialist during the study period, leaving 2 597 127 patients in the cohort. No data were missing except for rural status (0.5%) and lowest neighborhood income quintile (5.6%).

    The median (interquartile range) age of the participants was 46 (32-59) years and 54.1% were female. The median (interquartile range) number of comorbidities for all patients was 1 (0-2); 833 223 patients (32.1%) had more than 1 comorbidity; 476 079 (18.3%) had 3 or more comorbidities, and 146 993 (5.7%) had 5 or more comorbidities. Over 1 year of follow-up, 21 792 (0.8%) died, the median (range) days spent in the hospital was 0 (0-365) (211 384 [8.1%] with ≥1 hospitalization), and the median (interquartile range) number of prescribed medications was 3 (1-7). Baseline characteristics of the patients by physician group are shown in Table 1. Some specialties were more likely than others to see patients with characteristics that might contribute to complexity. For example, a greater proportion of older patients were seen by cardiologists, hematologists, and nephrologists. Patients of indigenous origin were most often seen by nephrologists, infectious disease specialists, and rheumatologists. Patients on social assistance were more often seen by infectious disease specialists, nephrologists, and neurologists. Patients residing in rural communities were more likely to see family physicians, nephrologists, and rheumatologists.

    Markers of Complexity by Physician Group

    There was substantial variability across physician groups for all 9 of the complexity markers (Table 2; eTable 1 in the Supplement). Patients seen by nephrologists had the highest mean number of comorbidities (4.2; 95% CI, 4.2-4.3 vs [lowest] 1.1; 95% CI, 1.0-1.1), highest mean number of prescribed medications (14.2; 95% CI, 14.2-14.3 vs [lowest] 4.9; 95% CI, 4.9-4.9), highest rate of death (6.6%; 95% CI, 6.3%-6.9% vs [lowest] 0.1%; 95% CI, <0.1%-0.2%), and highest rate of placement in a long-term care facility (2.0%; 95% CI, 1.8%-2.2% vs [lowest] <0.1%; 95% CI, <0.1%-0.1%); patients seen by infectious disease specialists had the highest complexity as assessed by the other 5 markers: rate of a mental health condition (29%; 95% CI, 28%-29% vs [lowest] 14%; 95% CI, 14%-14%), mean number of physician types (5.5; 95% CI, 5.5-5.6 vs [lowest] 2.1; 95% CI, 2.1-2.1), mean number of physicians (13.0; 95% CI, 12.9-13.1 vs [lowest] 3.8; 95% CI, 3.8-3.8), mean days in hospital (15.0; 95% CI, 14.9-15.0 vs [lowest] 0.4; 95% CI, 0.4-0.4), and mean emergency department visits (2.6; 95% CI, 2.6-2.6 vs [lowest] 0.5; 95% CI, 0.5-0.5).

    Between-group variability was most pronounced for mean number of days in the hospital and mean number of unique medications prescribed and least pronounced for long-term care placements and all-cause death (Table 2; eTable 1 in the Supplement). When complexity markers were expressed as the frequency of specific values rather than as means, these between-specialty differences became more apparent (eFigure 2 in the Supplement).

    There were clear trends in the average complexity of patients seen by physician type. Patients seen by infectious disease specialists, nephrologists, and neurologists were consistently more complex, and patients seen by endocrinologists, clinical allergists/immunologists, and dermatologists were consistently less complex (Table 2; eTable 1 in the Supplement). eFigure 3 in the Supplement expresses each of the complexity markers in relative terms (and Figure 1 expresses 3 of the complexity markers in relative terms), with each physician group compared with patients seen by family physicians. Overall ranking of patient complexity and individual ranking for each of the 9 complexity markers by physician group are shown in Figure 2. When types of physician were ranked according to patient complexity across all 9 markers, the order from most to least complex was nephrologist, infectious disease specialist, neurologist, respirologist, hematologist, rheumatologist, gastroenterologist, cardiologist, general internist, endocrinologist, allergist/immunologist, dermatologist, and family physician.

    Results were consistent in sensitivity analyses that used each visit as the unit of analysis (giving more weight to patients who were seen multiple times [eTable 2 in the Supplement]), required more than 1 claim to define being seen by a particular specialty (eTables 3 and 4 in the Supplement), or repeated all analyses in a different time period (basing the cohort on Alberta residence to April 1, 2009, rather than April 1, 2014 [eTable 5 in the Supplement]). Considerable variability between specialties remained in all analyses, although there was some variation in the rankings. When the visit was used as the unit of analysis (or >1 claim was required to define being seen), the relative ranking of general internists and family physicians tended to increase, whereas the complexity of nephrology patients remained first overall, and the complexity of patients seen by infectious disease specialists, respiratory specialists, and neurologists were consistently ranked in the top 5. Repeating analyses using the 2009 cohort did not change any of the conclusions.

    Discussion

    In keeping with our hypothesis, we found substantial differences in the average complexity of patients seen by different types of physician. Although no single specialty’s patients were most complex by all measures, patients seen by nephrologists, infectious disease specialists, and neurologists consistently tended to be more complex than others, whereas patients seen by other types of physician, such as clinical allergists, dermatologists, and family physicians, consistently tended to be less complex.

    There is no agreed definition of patient complexity.7 Most available instruments, such as the Vector Model of Complexity16 or the Patient Centered Assessment Method,2 assess patients according to domains such as health, social factors, health literacy, and service coordination, each of which includes 2 or more subitems. Clinical experience and the available literature suggest that overall complexity includes not just medical issues but also social characteristics and is influenced by contextual factors, such as the structure and organization of the underlying health system. Given that it was based on administrative data, our analysis focused chiefly on medical aspects of complexity, although we included certain socioeconomic characteristics such as income, rural residence location, indigenous origin, and residence in a lower-income neighborhood, all of which were again more common in infectious diseases specialists and nephrologists. Our analysis would have been strengthened by availability of data to allow direct assessment of characteristics such as coordination of care rather than proxies. For example, a direct question such as “Are the services involved with this client well coordinated?” (as recommended by the Patient Centered Assessment Method17) would provide better insight as to the true complexity of a particular patient than simply counting the number of physician types involved in that patient’s care (as we did). However, while our approach has limitations, it should not have led to bias unless the proxies that we used are more or less accurate in some specialties than in others.

    Although it seems widely accepted that the complexity of patients seen by different types of physician is highly variable, we did not identify other studies of this issue. Previous studies of complexity have tended to focus on the association between complexity (typically defined by number of morbidities alone) and clinical outcomes,18,19 or on the implications of complexity for health systems and health policy.16,20,21

    Our primary analysis used the characteristics of the average patient seen by each specialty to assess complexity, which arguably best reflects the workload associated with a typical day of practice. However, this approach could be criticized on the grounds that physicians have little impact on the care of complex patients that they see only once. Using the visit (eTable 2 in the Supplement) as the unit of analysis (thus, giving greater weight to the characteristics of patients who are seen multiple times) partially addresses this limitation, as does retaining the patient as the unit of analysis but only including patients who saw each type of physician more than once (eTables 3 and 4 in the Supplement). We took both of these approaches in sensitivity analyses and found a similar overall ranking of specialties as compared with the primary analysis, with slightly larger differences between specialties. Repeating the analyses with an earlier cohort of patients demonstrated that results were robust over time.

    The fact that the ranking was consistent regardless of the analytical approach taken should increase confidence in our findings. However, we believe that the relative rank of the different specialties we studied is less important than the finding that there are wide variations in complexity between specialties. The latter has potential implications for medical education and health policy. First, our findings suggest that skills in managing complex patients are more important for some specialties than for others, and that the skills required to care for complex patients should be considered when medical students choose a clinical specialty. Directors of residency programs in which complexity is especially common may consider the merits of including formal training on complexity, multimorbidity, and their implications. Second, there is no debate that patient complexity requires time (including the time required to communicate with the multiple other clinicians often involved in a patient’s care), expertise, and resources to optimize management. However, reimbursement of physicians and facilities in North America is most commonly based on fee-for-service compensation.4 In the fee-for-service payment structure, the type and duration of an encounter is the primary determinant of payment. The complexity of medical decision making is addressed by assessing the number of diagnoses and management options that are considered, the medical risks, and the amount of data to be reviewed. While easily ascertainable, these factors do not fully account for clinical complexity.22-24 Moreover, adjusting payments to encourage physicians or clinical programs to spend more time and resources caring for patients at highest risk of complications makes sense from a health care payer perspective. This is particularly important as health systems experiment with the use of bundled payment for hospital care for episodes of myocardial infarction or coronary artery bypass grafting, or for procedures like joint arthroplasty—where limited risk adjustment has been used to date.25,26 In view of our findings, policy makers should consider how funding for specialty-specific clinical programs and mechanisms for linking health care programs to social care initiatives could consider the complexity of patients more appropriately.25-27 This could be done by explicitly accounting for complexity when setting relative value units of evaluation and management codes22 as well as budgets for clinical programs, particularly in the context of bundled payments. Any such policy remedy would require careful consideration and rigorous evaluation in pilot testing before widespread adoption. Finally, we speculate that the observed differences in patient complexity may also contribute to differential burnout rates among medical specialties.28

    Our study has several important strengths, including the use of population-based data from a geographically defined area served by a universal health care system; a relatively large sample size; use of validated algorithms for ascertaining the presence or absence of comorbidity and clinical outcomes; rigorous analytical methods; and consideration of a broad range of proxies for patient complexity.

    Limitations

    Our study has limitations that should be considered when interpreting results. First, most of the authors of our study are nephrologists, and given the findings, there may be a perceived conflict of interest. We emphasize that the primary goal of this article was not to justify increased resources for kidney care programs specifically, but rather to propose a more nuanced consideration of how any health program is resourced in the face of increasing patient complexity. Second, like all studies using administrative data, some assumptions are required when assessing comorbidities, outcomes, and exposures. However, any misclassification should have been nondifferential and is unlikely to have affected the observed differences between physician types. In addition, it seems unlikely that nuances in billing practices or clinical practice patterns between different types of physician could completely explain the observed differences. Third, our data sources allowed us only to assess the presence or absence of comorbidity, rather than its severity. It is difficult to speculate how this might have affected our results, although it seems unlikely that better information on the severity of comorbidity would have affected our conclusions. Fourth, the presence of a comorbidity such as mental illness does not necessarily mean that physicians managed that comorbidity. Fifth, we studied people from a single Canadian province and our findings may not be generalizable to other health care settings. For example, in the United States, a lack of coordination between federal and state governments coupled with a complex mix of employer-sponsored and governmental health insurance could alter relative medical complexity by specialty. Sixth, we chose to include mortality and the likelihood of hospitalization as markers of complexity, although arguably these could be considered consequences of complexity instead. However, excluding these markers of complexity from our analysis would not have affected our main conclusions, especially if they were replaced with other candidate markers such as income, residence location, and indigenous origin. Seventh, and most important, we did not have data on other potentially important determinants of complexity such as adherence, opiate use, lack of fluency in one of Canada’s 2 official languages, health literacy, sensory impairment (eg, blindness or deafness), financial resources, or social networks.29

    Conclusions

    We found substantial between-specialty differences in 9 different markers of patient complexity. These findings have implications for medical education and health policy.

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    Article Information

    Accepted for Publication: September 20, 2018.

    Published: November 30, 2018. doi:10.1001/jamanetworkopen.2018.4852

    Correction: This article was corrected on March 1, 2019, to fix a wording error in Results and data errors in Table 1 and Figure 2.

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Tonelli M et al. JAMA Network Open.

    Corresponding Author: Marcello Tonelli, MD, SM, MSc, University of Calgary, 3280 Hospital Dr NW, TRW Bldg, Seventh Floor, Calgary, AB T2N 4Z6, Canada (tonelli.admin@ucalgary.ca).

    Author Contributions: Dr Tonelli had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Tonelli, Wiebe, Manns, James, Himmelfarb.

    Acquisition, analysis, or interpretation of data: Tonelli, Wiebe, Klarenbach, James, Ravani, Pannu, Hemmelgarn.

    Drafting of the manuscript: Tonelli, Wiebe.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Wiebe, James, Ravani, Hemmelgarn.

    Obtained funding: Tonelli, Manns.

    Administrative, technical, or material support: James, Hemmelgarn.

    Supervision: Tonelli, Wiebe.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: Dr Tonelli was supported by the David Freeze Chair in Health Services Research, Dr Manns was supported by the Svare Chair in Health Economics, and Dr Hemmelgarn was supported by the Baay Chair in Kidney Research, all at the University of Calgary. This work was supported by Foundation awards from the Canadian Institutes for Health Research (Drs Tonelli, Manns, James, and Hemmelgarn) by a team grant to the Interdisciplinary Chronic Disease Collaboration from Alberta Innovates and a Leaders Opportunity Fund grant from the Canada Foundation for Innovation.

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Disclaimer: This study is based in part by data provided by Alberta Health and Alberta Health Services. The interpretation and conclusions contained herein are those of the researchers and do not represent the views of the Government of Alberta or Alberta Health Services. Neither the Government of Alberta nor Alberta Health or Alberta Health Services express any opinion in relation to this study.

    Additional Contributions: Ghenette Houston, BA, University of Alberta, provided administrative support. She was compensated by her salary from the university.

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