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

      research-article
      , MD, SM, MSc 1 , , , MMath, PStat 2 , , MD, MSc 1 , , MD, MSc 2 , , MD, PhD 1 , , MD, PhD 1 , , MD, SM 2 , , MD 3 , , MD, PhD 1
      JAMA Network Open
      American Medical Association

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          Key Points

          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

          This cohort study compares 9 markers of patient complexity among patients treated by physicians in different specialties in Alberta, Canada, using data collected by the province’s universal health care system.

          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.

          Related collections

          Most cited references18

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          • Abstract: found
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          Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline.

          The Kidney Disease: Improving Global Outcomes (KDIGO) organization developed clinical practice guidelines in 2012 to provide guidance on the evaluation, management, and treatment of chronic kidney disease (CKD) in adults and children who are not receiving renal replacement therapy. The KDIGO CKD Guideline Development Work Group defined the scope of the guideline, gathered evidence, determined topics for systematic review, and graded the quality of evidence that had been summarized by an evidence review team. Searches of the English-language literature were conducted through November 2012. Final modification of the guidelines was informed by the KDIGO Board of Directors and a public review process involving registered stakeholders. The full guideline included 110 recommendations. This synopsis focuses on 10 key recommendations pertinent to definition, classification, monitoring, and management of CKD in adults.
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            • Article: not found

            Risk of coronary events in people with chronic kidney disease compared with those with diabetes: a population-level cohort study.

            Diabetes is regarded as a coronary heart disease risk equivalent-ie, people with the disorder have a risk of coronary events similar to those with previous myocardial infarction. We assessed whether chronic kidney disease should be regarded as a coronary heart disease risk equivalent. We studied a population-based cohort with measures of estimated glomerular filtration rate (eGFR) and proteinuria from Alberta, Canada. We used validated algorithms based on hospital admission and medical-claim data to classify participants with baseline history of myocardial infarction or diabetes and to ascertain which patients were admitted to hospital for myocardial infarction during follow-up (the primary outcome). For our primary analysis, we defined baseline chronic kidney disease as eGFR 15-59·9 mL/min per 1·73 m(2) (stage 3 or 4 disease). We used Poisson regression to calculate unadjusted rates and relative rates of myocardial infarction during follow-up for five risk groups: people with previous myocardial infarction (with or without diabetes or chronic kidney disease), and (of those without previous myocardial infarction), four mutually exclusive groups defined by the presence or absence of diabetes and chronic kidney disease. During a median follow-up of 48 months (IQR 25-65), 11,340 of 1,268,029 participants (1%) were admitted to hospital with myocardial infarction. The unadjusted rate of myocardial infarction was highest in people with previous myocardial infarction (18·5 per 1000 person-years, 95% CI 17·4-19·8). In people without previous myocardial infarction, the rate of myocardial infarction was lower in those with diabetes (without chronic kidney disease) than in those with chronic kidney disease (without diabetes; 5·4 per 1000 person-years, 5·2-5·7, vs 6·9 per 1000 person-years, 6·6-7·2; p<0·0001). The rate of incident myocardial infarction in people with diabetes was substantially lower than for those with chronic kidney disease when defined by eGFR of less than 45 mL/min per 1·73 m(2) and severely increased proteinuria (6·6 per 1000 person-years, 6·4-6·9 vs 12·4 per 1000 person-years, 9·7-15·9). Our findings suggest that chronic kidney disease could be added to the list of criteria defining people at highest risk of future coronary events. Alberta Heritage Foundation for Medical Research. Copyright © 2012 Elsevier Ltd. All rights reserved.
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              Methods for identifying 30 chronic conditions: application to administrative data

              Background Multimorbidity is common and associated with poor clinical outcomes and high health care costs. Administrative data are a promising tool for studying the epidemiology of multimorbidity. Our goal was to derive and apply a new scheme for using administrative data to identify the presence of chronic conditions and multimorbidity. Methods We identified validated algorithms that use ICD-9 CM/ICD-10 data to ascertain the presence or absence of 40 morbidities. Algorithms with both positive predictive value and sensitivity ≥70% were graded as “high validity”; those with positive predictive value ≥70% and sensitivity <70% were graded as “moderate validity”. To show proof of concept, we applied identified algorithms with high to moderate validity to inpatient and outpatient claims and utilization data from 574,409 people residing in Edmonton, Canada during the 2008/2009 fiscal year. Results Of the 40 morbidities, we identified 30 that could be identified with high to moderate validity. Approximately one quarter of participants had identified multimorbidity (2 or more conditions), one quarter had a single identified morbidity and the remaining participants were not identified as having any of the 30 morbidities. Conclusions We identified a panel of 30 chronic conditions that can be identified from administrative data using validated algorithms, facilitating the study and surveillance of multimorbidity. We encourage other groups to use this scheme, to facilitate comparisons between settings and jurisdictions. Electronic supplementary material The online version of this article (doi:10.1186/s12911-015-0155-5) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                30 November 2018
                November 2018
                30 November 2018
                : 1
                : 7
                : e184852
                Affiliations
                [1 ]Department of Medicine, University of Calgary, Calgary, Alberta, Canada
                [2 ]Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
                [3 ]Department of Medicine, University of Washington, Seattle
                Author notes
                Article Information
                Accepted for Publication: September 20, 2018.
                Published: November 30, 2018. doi:10.1001/jamanetworkopen.2018.4852
                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@ 123456ucalgary.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.
                Article
                zoi180212
                10.1001/jamanetworkopen.2018.4852
                6324421
                30646392
                d1da82bd-c79b-4278-a236-1474be351dc2
                Copyright 2018 Tonelli M et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 1 August 2018
                : 19 September 2018
                : 20 September 2018
                Categories
                Research
                Original Investigation
                Online Only
                Health Policy

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