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Original Research |

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

Richard W. Grant, MD, MPH; Jeffrey M. Ashburner, MPH; Clemens S. Hong, MD, MPH; Yuchiao Chang, PhD; Michael J. Barry, MD; and Steve J. Atlas, MD, MPH
[+] Article and Author Information

From the Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.

Acknowledgment: The authors thank Lulu Y. Liu, MS, from the General Medicine Division, for her assistance with preparing the manuscript; Jennifer M. Luttrell from the Laboratory of Computer Science, for creating the online patient review tool; and the primary care providers of the Primary Care Operations Improvement Advisory Board, Massachusetts General Hospital, for clinical insights and valuable feedback during the project.

Grant Support: By the Systems Improvement Grant Program of Partners Community Healthcare, Inc.

Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M11-0862.

Reproducible Research Statement:Study protocol and statistical code: Available from Dr. Atlas (e-mail, satlas@partners.org). Data set: Not available.

Requests for Single Reprints: Richard W. Grant, MD, MPH, Kaiser Permanente, Division of Research, 2101 Webster Street, 20th Floor, Oakland, CA 94612; e-mail, Richard.W.Grant@KP.org.

Current Author Addresses: Dr. Grant: Kaiser Permanente, Division of Research, 2101 Webster Street, 20th Floor, Oakland, CA 94612.

Mr. Ashburner and Drs. Hong, Chang, Barry, and Atlas: General Medicine Division, Massachusetts General Hospital, 50 Staniford Street, 9th Floor, Boston, MA 02114.

Author Contributions: Conception and design: R.W. Grant, M.J. Barry, S.J. Atlas.

Analysis and interpretation of the data: R.W. Grant, J.M. Ashburner, C.S. Hong, Y. Chang, S.J. Atlas.

Drafting of the article: R.W. Grant, J.M. Ashburner.

Critical revision of the article for important intellectual content: J.M. Ashburner, C.S. Hong, M.J. Barry, S.J. Atlas.

Final approval of the article: R.W. Grant, J.M. Ashburner, C.S. Hong, M.J. Barry, S.J. Atlas.

Provision of study materials or patients: R.W. Grant, J.M. Ashburner.

Statistical expertise: Y. Chang.

Obtaining of funding: R.W. Grant.

Administrative, technical, or logistic support: J.M. Ashburner, M.J. Barry.

Collection and assembly of data: R.W. Grant, J.M. Ashburner, S.J. Atlas.


Ann Intern Med. 2011;155(12):797-804. doi:10.7326/0003-4819-155-12-201112200-00001
Text Size: A A A

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.

Figures

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Figure.

Prevalence (95% CI) of complexity domains among 1126 PCP-defined complex patients, by patient age.

P < 0.001 for trends by decade. PCP = primary care physician.

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Appendix Figure 1.

Agreement between PCP-defined complexity and Charlson score (threshold score of 1) for each PCP, grouped by practice type.

PCP = primary care physician.

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Appendix Figure 2.

Agreement between PCP-defined complexity and Charlson score (threshold score of 2) for each PCP, grouped by practice type.

PCP = primary care physician.

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Appendix Figure 3.

Agreement between PCP-defined complexity and Higashi score (threshold score of 2) for each PCP, grouped by practice type.

PCP = primary care physician.

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Appendix Figure 4.

Agreement between PCP-defined complexity and CMS hospitalization risk algorithm (threshold relative risk of 3) for each PCP.

CMS = Centers for Medicare & Medicaid Services; PCP = primary care physician.

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Tables

References

Letters

April 17, 2012
Marcel G.M. Olde Rikkert, MD, PhD; Henk J. Schers, MD, PhD; Rene J.F. Melis, MD, PhD
AIM. 2012;156(8):606  doi:10.7326/0003-4819-156-8-201204170-00001



April 17, 2012
Joseph M. Cerimele, MD; Lauren A. Peccoralo, MD, MPH
AIM. 2012;156(8):606-607  doi:10.7326/0003-4819-156-8-201204170-00016



April 17, 2012
Richard W. Grant, MD, MPH; Clemens S. Hong, MD, MPH; Steven J. Atlas, MD, MPH
AIM. 2012;156(8):607  doi:10.7326/0003-4819-156-8-201204170-00002



NOTE:
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The importance of primary care based psychiatric services
Posted on December 28, 2011
Joseph M., Cerimele, MD, Resident physician, Lauren A. Peccoralo, MD, MPH
Mount Sinai School of Medicine
Conflict of Interest: None Declared

Grant, et al. (1) highlighted the significant role played by psychiatric and substance use disorders in moderating primary care physicians' impressions of patient complexity. Furthermore, they noted that the binary presence of a disorder (e.g. the presence or absence of diabetes) did not contribute to patient complexity as much as the "complex interplay among disease severity, medical treatment, and patient management behaviors." The Agency for Healthcare Research and Quality (2) has also recognized the factors contributing to patient complexity, and noted the need for and importance of integrating psychiatric services into primary care settings to address these factors.

Collaborative care is a primary care based intervention aiming to increase a population's exposure to effective psychiatric care through the use of patient registries, nurse care managers supervised by psychiatrists, treat-to-target guidelines, frequent case review and other methods. Many randomized controlled trials have demonstrated the effectiveness of collaborative care in treating depression in primary care, though one recent study (3) showed that collaborative care is also effective in managing both depression and chronic illnesses in complex patients.

Katon, et al (3) randomized 214 complex patients ("with multiple, poorly controlled chronic diseases - diabetes and/or coronary artery disease - complicated by psychological and behavioral impairments including depression, unhealthy lifestyle and poor adherence to medication regimens" ) from 14 sites to 12 months of treatment in a collaborative care model or usual primary care. Patients in the intervention group had significantly greater improvements in depression symptoms measured by the Symptoms Checklist-20 (SCL-20) (difference of 0.40 points) diabetes care measured by HbA1c percentages (difference of 0.58%), and coronary heart disease care measured by LDL cholesterol levels (difference of 6.9mg/deciliter) and systolic blood pressure (difference of 5.1mm Hg,). Furthermore, at 12 months, more patients in the intervention group reported improved quality of life (45% vs 18%) and greater overall satisfaction with depression and chronic illness care (90% vs. 55% and 86% vs 70%, respectively). Though the investigators did not report cost effectiveness studies from this trial, they previously reported cost- savings with collaborative care treatment of patients with depression and diabetes (4).

Overall, some of the factors leading to patient complexity identified by Grant, et al. (e.g. mental health problems, substance use, psychosocial problems) can be addressed and managed by delivering psychiatric services in primary care settings using models such as collaborative care.

References

1. Grant RW, Ashburner JM, Hong CC, Chang Y, Barry MJ, Atlas SJ. Defining patient complexity from the primary care physician's perspective. Ann Intern Med. 2011;155:797-804

2. Croghan TW, Brown JD. Integrating mental health treatment into the patient centered medical home. (prepared by Mathematica policy research under contract no. HHSA290200900019ITO2.) AHRQ Publication No. 10-0084-EF. Rockville, MD: Agency for Healthcare Research and Quality. June 2010.

3. Katon WJ, Lin EHB, Von Korff M Ciechanowski P, Ludman EJ, Young B, et al. Collaborative care for patients with depression and chronic illnesses. N Engl J Med. 2010; 363:2611-2620

4. Simon GE, Katon WJ, Lin EHB, et al. Cost-effectiveness of systematic depression treatment among people with diabetes mellitus. Arch Gen Psychiatry. 2007; 64: 65-72

Conflict of Interest:

None declared

Complexity: does it tell more than frailty?
Posted on January 3, 2012
Marcel GM, Olde Rikkert, Professor in Geriatric Medicine (1), Henk J Schers (2), Rene JF Melis (1)
Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands, Departments of Geriatric Medi
Conflict of Interest: None Declared

Grant et al. carried out important research by analyzing what determined complexity as judged by primary care physicians (PCP) in community-dwelling subjects (1). The authors show clearly that the involved complexity domains vary strongly for subjects' age. In the elderly, PCP- perceived complexity is related mainly to problems in medical decision making and coordination of care. This certainly relates to the vulnerability of a subgroup of elderly patients. Our major criticism on this study is that it does not at all take into account the term 'frailty', which has been intensely studied during the last decade to differentiate the large heterogeneity of the elderly patient. We will shortly point out why it is not logical to just mention complexity without relating it to the frailty paradigm in the elderly.

In Grant's study, primary care physicians had to indicate which patients "in their view" were complex. Thereby, the researchers not only relied on the PCPs' tacit knowledge on patients, but also on their conception of the term complexity. We think that many of the patients that were judged 'complex', would also have been 'frail' according to the PCPs. Frailty refers to subjects who are likely to experience negative health outcomes in the near future by limited external stressors. This definition fits closely to the complexity term, which according to Nobel Prize winner Prigogine, refers to systems that work non-linear, with multiple scale dimensions, in network dynamics, and in a probabilistic nature (2). Although frailty is not yet prospectively studied in RCTs to define its added value in screening and triaging older subjects, it is increasingly evidenced by both clinical and epidemiological research. Even empirical data collection on frailty in primary care has been started (3).

The authors conclude rightly that he use of data from an existing registration network was a limitation and hampered the collection of prospective data. However, the frailty-index defined by Rockwood et al can be used to calculate the cumulative deficit per subject just by using an existing database (4). Grant et al might still be able to post-hoc calculate the frailty index of their population, and compare the groups defined as frail and non-frail to the groups defined by complexity state. This would be worthwhile the effort, because as long as the terms frailty and complexity are not studied together, we do not know whether using both is really rendering added value.

References

1. Grant RW, Ashburner JM, Hong CC, Chang Y, Barry MJ, Atlas SJ. Defining Patient Complexity From the Primary Care Physician's Perspective: A Cohort Study. Ann Intern Med. 2011;155:797-804.

2. Prigogine, From Being to Becoming. Time and Complexity in the Physical Sciences, Freeman, CA, 1980.

3. Romero-Ortuno R, Walsh CD, Lawlor BA, Kenny RA. A frailty instrument for primary care: findings from the Survey of Health, Ageing and Retirement in Europe (SHARE). BMC Geriatrics. 2010;10(57).

4. Rockwood K, Mitnitski A. Frailty defined by deficit accumulation and geriatric medicine defined by frailty. Clin Geriatr Med 2011;27:17-26

Conflict of Interest:

None declared

Author's Reply
Posted on February 3, 2012
Richard W., Grant, MD, MPH, Clemens S. Hong, MD, MPH, Steven J. Atlas, MD, MPH
Conflict of Interest: None Declared

Rikkert and Schers address the challenge of identifying individuals at high risk for negative health outcomes. While we focused on complexity, they argue that frailty may represent another construct physicians could use to identify such patients, and cite a method by Rockwood and Mitnitski that uses electronically-available data such as prescriptions and diagnoses to assess cumulative frailty over time. We agree that frailty is an important concept in the care of elderly patients that may have important implications for screening and treatment. While there is no doubt some overlap between complexity and frailty, particularly among older individuals, the concept of frailty is not likely to be as applicable to the many younger patients in our study (mean age 50.9 years). We agree that future work should consider how complexity and frailty overlap, and there is need to validate Rockwood and Mitnitski's method of measuring frailty. Whether the construct of patient complexity or frailty is more useful in primary care management may be something worthy of further investigation.

Cerimele and Peccoralo raise the issue of interventions for complex patients. They emphasize the need for more effective collaboration to address mental health problems among primary care patients. Many patients and primary care doctors find it difficult to obtain timely psychiatric care, and we agree that there is need to redesign primary care health delivery systems to more effectively address these barriers. Katon's study of coordinated care for patients with diabetes or cardiovascular disease and depression represents, in our view, a useful starting point towards a model of primary care that coordinates and supports the management of patients with multiple related (or unrelated) conditions. Among patients with multiple concurrent conditions, not all will require intensive collaborative management. Our study provides insight into which of these seemingly complex patients actually represent challenges to primary care management and therefore provides a way forward for approaching efficient health delivery redesign.

Richard W. Grant, MD, MPH Clemens S. Hong, MD, MPH Steven J. Atlas, MD, MPH

Conflict of Interest:

None declared

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