Clemens S. Hong

Defining Patient Complexity From the Primary Care Physician's Perspective: A Cohort Study

BACKGROUND Patients with complex health needs are increasingly the focus of health system redesign. OBJECTIVE To characterize complex patients, as defined by their primary care physicians (PCPs), and to compare this definition with other commonly used algorithms. DESIGN Cohort study. SETTING 1 hospital-based practice, 4 community health centers, and 7 private practices in a primary care network in the United States. PARTICIPANTS 40 physicians who reviewed a random sample of 120 of their own patients. MEASUREMENTS After excluding patients for whom they were not directly responsible, PCPs indicated which of their patients they considered complex. These patients were characterized, independent predictors of complexity were identified, and PCP-defined complexity was compared with 3 comorbidity-based methods (Charlson score, Higashi score, and a proprietary Centers for Medicare & Medicaid Services algorithm). RESULTS Physicians identified 1126 of their 4302 eligible patients (26.2%) as complex and assigned a mean of 2.2 domains of complexity per patient (median, 2.0 [interquartile range, 1 to 3]). Mental health and substance use were identified as major issues in younger complex patients, whereas medical decision making and care coordination predominated in older patients (P<0.001 for trends by decade). Major independent predictors of PCP-defined complexity (P<0.001) included age (probability of complexity increased from 14.8% to 19.8% with age increasing from 55 to 65 years), poorly controlled diabetes (from 12.7% to 47.6% if hemoglobin A1c level≥9%), use of antipsychotics (from 12.7% to 31.8%), alcohol-related diagnoses (from 12.9% to 27.4%), and inadequate insurance (from 12.5% to 19.2%). Classification agreement for complex patients ranged from 26.2% to 56.0% when PCP assignment was compared with each of the other methods. LIMITATION Results may not be generalizable to other primary care settings. CONCLUSION Primary care physicians identified approximately one quarter of their patients as complex. Medical, social, and behavioral factors all contributed to PCP-defined complexity. Physician-defined complexity had only modest agreement with 3 comorbidity-based algorithms. PRIMARY FUNDING SOURCE Partners Community Healthcare, Inc.

Relationship between patient panel characteristics and primary care physician clinical performance rankings.

CONTEXT Physicians have increasingly become the focus of clinical performance measurement. OBJECTIVE To investigate the relationship between patient panel characteristics and relative physician clinical performance rankings within a large academic primary care network. DESIGN, SETTING, AND PARTICIPANTS Cohort study using data from 125,303 adult patients who had visited any of the 9 hospital-affiliated practices or 4 community health centers between January 1, 2003, and December 31, 2005, (162 primary care physicians in 1 physician organization linked by a common electronic medical record system in Eastern Massachusetts) to determine changes in physician quality ranking based on an aggregate of Health Plan Employer and Data Information Set (HEDIS) measures after adjusting for practice site, visit frequency, and patient panel characteristics. MAIN OUTCOME MEASURES Composite physician clinical performance score based on 9 HEDIS quality measures (reported by percentile, with lower scores indicating higher quality). RESULTS Patients of primary care physicians in the top quality performance tertile compared with patients of primary care physicians in the bottom quality tertile were older (51.1 years [95% confidence interval {CI}, 49.6-52.6 years] vs 46.6 years [95% CI, 43.8-49.5 years], respectively; P < .001), had a higher number of comorbidities (0.91 [95% CI, 0.83-0.98] vs 0.80 [95% CI, 0.66-0.95]; P = .008), and made more frequent primary care practice visits (71.0% [95% CI, 68.5%-73.5%] vs 61.8% [95% CI, 57.3%-66.3%] with >3 visits/year; P = .003). Top tertile primary care physicians compared with the bottom tertile physicians had fewer minority patients (13.7% [95% CI, 10.6%-16.7%] vs 25.6% [95% CI, 20.2%-31.1%], respectively; P < .001), non-English-speaking patients (3.2% [95% CI, 0.7%-5.6%] vs 10.2% [95% CI, 5.5%-14.9%]; P <.001), and patients with Medicaid coverage or without insurance (9.6% [95% CI, 7.5%-11.7%] vs 17.2% [95% CI, 13.5%-21.0%]; P <.001). After accounting for practice site and visit frequency differences, adjusting for patient panel factors resulted in a relative mean change in physician rankings of 7.6 percentiles (95% CI, 6.6-8.7 percentiles) per primary care physician, with more than one-third (36%) of primary care physicians (59/162) reclassified into different quality tertiles. CONCLUSION Among primary care physicians practicing within the same large academic primary care system, patient panels with greater proportions of underinsured, minority, and non-English-speaking patients were associated with lower quality rankings for primary care physicians.

Toward Increased Adoption of Complex Care Management

Increasing evidence supports using specially trained, primary care–integrated, complex care management teams to improve outcomes and reduce costs by addressing the needs of high-cost patients. Yet substantial barriers to more widespread adoption remain.

Characteristics of “Complex” Patients With Type 2 Diabetes Mellitus According to Their Primary Care Physicians

Author Affiliations: Edmond J. Safra Center for Ethics, Harvard University, Cambridge, Massachusetts (Dr PhamKanter); Mongan Institute for Health Policy, Massachusetts General Hospital, Boston (Dr Pham-Kanter); Department of Health Systems, Management, and Policy, Colorado School of Public Health (Dr Pham-Kanter), and Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences (Dr Nair), University of Colorado Anschutz Medical Campus, Aurora; Department of Economics (Dr Pham-Kanter), University of Colorado Denver, Denver; and Department of Epidemiology, The Johns Hopkins University, Baltimore, Maryland (Dr Alexander). Correspondence: Dr Nair, Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Mail Stop C238, 12850 E Montview Blvd, Room V20-1202, Aurora, CO 80045 (kavita.nair @ucdenver.edu). Author Contributions: Dr Pham-Kanter 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. Study concept and design: Pham-Kanter. Acquisition of data: Pham-Kanter and Nair. Analysis and interpretation of data: Pham-Kanter, Alexander, and Nair. Drafting of the manuscript: Pham-Kanter, Alexander, and Nair. Critical revision of the manuscript for important intellectual content: Pham-Kanter, Alexander, and Nair. Statistical analysis: Pham-Kanter. Obtained funding: PhamKanter, Alexander, and Nair. Administrative, technical, and material support: Pham-Kanter and Nair. Study supervision: Pham-Kanter and Nair. Financial Disclosure: Dr Alexander is a consultant for IMS Health and has served as an ad hoc member of the US FDA’s Drug Safety and Risk Management Advisory Committee. Dr Nair is a consultant for Janssen Services. Funding/Support: This research was supported by the Edmond J. Safra Center for Ethics at Harvard University. Dr Alexander is supported by awards from the Agency for Healthcare Research and Quality (R01 HS0189960) and the National Heart, Lung, and Blood Institute (R01 HL 107345-01). Role of the Sponsors: The funding sources had no role in the design and conduct of the study; analysis or interpretation of the data; or preparation or final approval of the manuscript prior to publication. Disclaimer: The contents of and opinions expressed in this report are solely the responsibility of the authors and do not necessarily represent the official views of the funders. Additional Contributions: Richard Read Allen, MS, Igor Gorlach, BA, Matthew Lemieux, and Grace Njau, BS, acquired some of the data required for the analysis. We wish to acknowledge input from members of the Research Ethics Program of Harvard Catalyst, the Harvard Clinical and Translational Science Center (NIH Award UL1 RR 025758 and financial contributions from Harvard University and its affiliated academic health care centers).

Addressing basic resource needs to improve primary care quality: a community collaboration programme

Background Unmet basic resource needs, such as difficulty affording healthcare, medications, food and housing, may contribute to worse healthcare quality indicators, but interventions are hampered by lack of specific knowledge regarding the distribution of unmet basic resource needs and their association with priority clinical conditions and health service use patterns. Methods Cross-sectional study of primary care patients in two urban academic practices from 1 October 2013 to 30 April 2014. Patients were screened for unmet needs and enrolled in a programme to link them with community resources. Key measures included patient report of unmet basic resource needs, clinical conditions prioritised by quality improvement programmes (hypertension, diabetes and depression), and health service use patterns such as frequent emergency department (ED) visits (>2 in the preceding year) and frequent clinic ‘no-shows’ (>1 in the preceding year). Results 416 patients with unmet needs were included, and compared with 2750 patients who did not report needs. The most common types of needs reported were: difficulties affording healthcare (46.5%), food (40.1%) and utilities (36.3%). Patients who reported unmet needs were more likely to have depression (17.8% vs 9.5%, p<0.0001), diabetes (32.7% vs 20.4%, p<0.0001), hypertension (54.3% vs 46.3%, p=0.002), be frequent ED users (11.3% vs 5.4%, p<0.0001), and have frequent ‘no-shows’ to clinic (21.6% vs 11.9%, p<0.0001). Conclusions Difficulty affording healthcare and food are particularly common needs among patients with priority conditions. Strategies to identify and address unmet needs as part of routine care may be an important way to improve healthcare quality.

Limited English Proficient Patients and Time Spent in Therapeutic Range in a Warfarin Anticoagulation Clinic

Background While anticoagulation clinics have been shown to deliver tailored, high‐quality care to patients receiving warfarin therapy, communication barriers with limited English proficient (LEP) patients may lead to disparities in anticoagulation outcomes. Methods and Results We analyzed data on 3770 patients receiving care from the Massachusetts General Hospital Anticoagulation Management Service (AMS) from 2009 to 2010. This included data on international normalized ratio (INR) tests and patient characteristics, including language and whether AMS used a surrogate for primary communication. We calculated percent time in therapeutic range (TTR for INR between 2.0 and 3.0) and time in danger range (TDR for INR <1.8 or >3.5) using the standard Rosendaal interpolation method. There were 241 LEP patients; LEP patients, compared with non‐LEP patients, had a higher number of comorbidities (3.2 versus 2.9 comorbidities, P=0.004), were more frequently uninsured (17.0% versus 4.3%, P<0.001), and less educated (47.7% versus 6.0% ≤high school education, P<0.001). LEP patients compared with non‐LEP patients spent less TTR (71.6% versus 74.0%, P=0.007) and more TDR (12.9% versus 11.3%, P=0.018). In adjusted analyses, LEP patients had lower TTR as compared with non‐LEP patients (OR 1.5, 95% CI [1.1, 2.2]). LEP patients who used a communication surrogate spent less TTR and more TDR. Conclusion Even within a large anticoagulation clinic with a high average TTR, a small but significant decrease in TTR was observed for LEP patients compared with English speakers. Future studies are warranted to explore how the use of professional interpreters impact TTR for LEP patients.

Outcomes of primary care patients who are frequent and persistent users of the ED.

nance imaging (MRI) is an additional tool. Antemortem MRI of the brain in rabies shows exclusive predilection for the gray matter including the basal ganglia, thalami, pontine, and midbrain nuclei [4]. Both the paralytic and furious forms of rabies have been reported to have similar distribution of signal changes on MRI [5]. Thus, imaging aids to thediagnosis of rabies evenbefore the appearanceof hydrophobia and/or aerophobia. This is in contrast to the predominant white matter involvement in “postimmunization acute disseminated encephalomyelitis.” Thus, MRI changes help differentiate it from many other viral encephalitides [6]. Jain et al [1] described the MRI findings in their case as hyperintense signal on T2-weighted image involving predominantly central gray matter of spinal cord with relative sparing of the white matter, which makes us consider the diagnosis of rabies and not as reported by the authors. Rupprecht et al [7] have devised an algorithmic approach to the neurologic manifestations and imaging features of rabies encephalitis and other common viral encephalitides. In short,MRIfindings ofmidline encephalitis should alert the physician in tropics to the possibility of rabies and take necessary steps immediately. Furthermore, anMRImaybeausedasdiagnosticmodality especiallywhen rabies vs acute disseminated encephalomyelitis is considered at the bed side, as the latter being a potentially treatable condition.