Currently Viewing:
The American Journal of Managed Care February 2017
Synchronized Prescription Refills and Medication Adherence: A Retrospective Claims Analysis
Jalpa A. Doshi, PhD; Raymond Lim, MA; Pengxiang Li, PhD; Peinie P. Young, PharmD, BCACP; Victor F. Lawnicki, PhD; Andrea B. Troxel, ScD; and Kevin G. Volpp, MD, PhD
Addressing the Chronification of Disease
Michael E. Chernew, PhD, Co-Editor-in-Chief, The American Journal of Managed Care
Economic Burden of Hypoglycemia With Basal Insulin in Type 2 Diabetes
Vivian Fonseca, MD; Engels Chou, MS; Hsing-Wen Chung, PhD; and Charles Gerrits, PhD, PharmD
Treating Medicaid Patients With Hepatitis C: Clinical and Economic Impact
Zobair Younossi, MD; Stuart C. Gordon, MD; Aijaz Ahmed, MD; Douglas Dieterich, MD; Sammy Saab, MD; and Rachel Beckerman, PhD
An Examination of the Relationship Between Care Management With Coaching for Activation and Patient Outcomes
Cindy Reistroffer, DSc; Larry R. Hearld, PhD; and Jeff M. Szychowski, PhD
Sustained Participation in a Pay-for-Value Program: Impact on High-Need Patients
Dori A. Cross, BSPH; Genna R. Cohen, PhD; Christy Harris Lemak, PhD; and Julia Adler-Milstein, PhD
Value-Based Contracting Innovated Medicare Advantage Healthcare Delivery and Improved Survival
Aloke K. Mandal, MD, PhD; Gene K. Tagomori, BSc; Randell V. Felix, BSc; and Scott C. Howell, DO, MPH&TM
Community-Based Asthma Education
Rohini Rau-Murthy, BA; Leslie Bristol, RRT, AE-C; and David Pratt, MD, MPH
Perceptions of the Medical Home by Parents of Children With Chronic Illnesses
Emily B. Vander Schaaf, MD, MPH; Elisabeth P. Dellon, MD, MPH; Rachael A. Carr, BA; Neal A. deJong, MD; Asheley C. Skinner, PhD; and Michael J. Steiner, MD, MPH
Patient Characteristics and Healthcare Utilization of a Chronic Pain Population Within an Integrated Healthcare System
Robert J. Romanelli, PhD; Sonali N. Shah, RPh, MBA, MPH; Laurence Ikeda, MD; Braden Lynch, PharmD, MS, CPEHR; Terri L. Craig, PharmD, CPEHR; Joseph C. Cappelleri, PhD, MPH, MS; Trevor Jukes, MS; and D
Currently Reading
Patients With Diabetes in Pay-for-Performance Programs Have Better Physician Continuity of Care and Survival
Chien-Chou Pan, MD, PhD; Pei-Tseng Kung, ScD; Li-Ting Chiu, MHA; Yu Pei Liao, MHA; and Wen-Chen Tsai, DrPH

Patients With Diabetes in Pay-for-Performance Programs Have Better Physician Continuity of Care and Survival

Chien-Chou Pan, MD, PhD; Pei-Tseng Kung, ScD; Li-Ting Chiu, MHA; Yu Pei Liao, MHA; and Wen-Chen Tsai, DrPH
Patients with diabetes who participate in a pay-for-performance program had higher continuity of care index (COCI) scores, and those with high COCI scores had higher survival rates.

Objectives: This study investigated the effects of physician continuity, measured as the Continuity of Care Index (COCI) score, on the survival of patients with diabetes, including both pay-for-performance (P4P) participants and nonparticipants.
Study Design: This was a retrospective, nationwide population-based analysis of 396,838 patients with diabetes, with 198,419 subjects each in the P4P participant and nonparticipant groups, from 1997 to 2009, in Taiwan.
Methods: The data presented in this study are secondary data obtained from the 1997 to 2009 National Health Insurance Research Database published by the Taiwan National Health Research Institute. Survival status and physician continuity were the dependent variables. Multiple regression analysis was used to examine the factors related to physician continuity among patients with diabetes. The Cox proportional hazard model was used to explore the related factors that affected the survival status of the patients with diabetes.
Results: After controlling for the other related factors, the COCI score of the P4P participants was 0.227 higher than that of the nonparticipants (P <.05). Compared with patients with a low COCI score (≤50%), the hazard ratio (HR) of mortality of patients with a high COCI score (>50%) was 0.47 (95% confidence interval [CI], 0.46-0.48). Compared with nonparticipants, the HR of mortality of P4P participants was 0.43 (95% CI, 0.41-0.44).
Conclusions: Patients with diabetes with higher physician continuity had a lower HR of mortality. P4P participants had higher physician continuity and a lower HR of mortality. 

Am J Manag Care. 2017;23(2):e57-e66
Take-Away Points
  • Diabetes pay-for-performance programs had higher physician continuity of care.
  • Patients with diabetes with higher physician continuity had lower mortality. 
  • For other chronic diseases, such as hypertension or obesity, pay-for-performance programs can be applied and might also increase patients’ physician continuity and survival.
Diabetes is a common chronic metabolic disease with increasing prevalence.1 The strategies to improve diabetes care include optimizing the behavior of health providers, supporting patient behavioral adaptations, and changing the system of care.2 With the increasing number of patients with diabetes, the provision of high-quality and cost-effective care for these patients has become an important but difficult task for healthcare providers.3 The healthcare systems of many countries have implemented pay-for-performance (P4P) programs to improve the quality of care. To improve patient outcomes, a P4P program provides ļ¬nancial rewards to healthcare providers who achieve pre-established criteria for treating specific diseases.4

In Taiwan, the National Health Insurance (NHI) Administration implemented its diabetes P4P program in 2001. Physicians whose specialties were internal medicine, pediatrics, family medicine, metabolism and endocrinology, cardiology, or nephrology were allowed to voluntarily participate.5 The P4P program provides financial incentives with additional reimbursements, which include increasing physician fees and case management fees to the participating physicians. In this program, quality performance is monitored by 4 indicators—each representing 25% of total achievement—that include the rates of patients: 1) who completed regular follow-ups (at least 3 visits per year), 2) whose glycated hemoglobin (A1C) was lower than 7%, 3) whose A1C was higher than 9.5%, and 4) whose low-density lipoprotein (LDL) was higher than 130 mg/dL. The achievement rate of each indicator was then summed and became the final achievement grade. Physicians were ranked according to this final achievement. Physicians can receive additional bonuses if they are ranked in the top 25% of their peers.6 Under this program, physicians are required to keep their P4P participants in follow-up at their clinic. If the P4P participants change doctors, the physician bonuses will be decreased or cancelled.

Taiwan’s NHI program has no strict referral system; patients can go to any hospital or see any doctor of any subspecialty. However, changing physicians frequently seems to result in adverse outcomes. McAlister et al found that the mortality risk of patients with heart failure was lower among those who visited familiar physicians.7 Younge et al found that in patients with diabetes, a lower modified Continuity of Care Index (COCI) score was associated with poorer A1C control and a higher modified COCI score was associated with better LDL control.8 Physician continuity is also a factor related to lower healthcare costs. De Maeseneer et al found that patients visiting the same family physician had lower total medical costs.9 In a diabetes P4P program, there should be many factors, including physician continuity, that would have an effect on the clinical outcomes and survival of treated patients; however, to our knowledge, no papers have discussed this topic until now.

This study was designed to investigate factors that influence the participation of patients with diabetes in the P4P program. The objectives of the study are to explore the differences in physician continuity of care and survival between diabetes P4P participants and nonparticipants and to investigate the related factors influencing physician continuity of care and survival among diabetes P4P participants.


Data Source and Research Subjects

The data presented in this retrospective study are secondary data obtained from the 1997 to 2009 NHI Research Database published by the National Health Research Institute. The study population included only patients newly diagnosed with type 2 diabetes between 2001 and 2009. Such a patient was defined as one hospitalized a minimum of 1 time or who was diagnosed with diabetes (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 250.x or A181) a minimum of 3 times at outpatient visits within 365 consecutive days (including both the primary and secondary diagnosis).10 Patients with gestational diabetes (ICD-9-CM code 648.0 or 648.8), neonatal diabetes (ICD-9-CM code 775.1), or impaired glucose tolerance (ICD-9-CM code 790.2) were excluded. A total of 1,179,862 patients were included in the study. The medical records of the follow-up outpatient visits of patients who had participated in the P4P program were reviewed and the specific treatment code E4 (P4P program for diabetes) was used as an indicator for these patients. Patients with diabetes who did not participate in the program for a minimum of half a year (26 weeks) were also excluded from the study.

In addition, personal factors and hospital policy could affect the participation of patients with diabetes in the P4P program, and thus, selection bias might occur.11 We adopted the following method to reduce selection bias between patients with diabetes who had or had not participated in the program. This method had been widely used in studies based on the Taiwan NHI database.12,13 The propensity score-matching method was used to collect a total of 396,830 research samples in 1:1 matching for further analysis based on personal characteristics (ie, gender and age), income (ie, monthly salary), severity of comorbidity (ie, Charlson Comorbidity Index [CCI] score14), severity of diabetes (Diabetes Complications Severity Index [DCSI] score15), and the characteristics of the healthcare organizations (ie, accreditation level and ownership).

Definitions of Variables and Method of Measurement

Two dependent variables were included in the study. The first was the survival status of the patients. Based on death certificate records, insurance withdrawal dates are the same as death dates. Taiwan’s NHI is a compulsory single-payer program and there are only 3 conditions for people being withdrawn from NHI coverage: those who have been jailed for more than 2 months, have been legally missing for more than 6 months, or have died. Death was defined as being withdrawn from the NHI coverage within 30 days of hospital admission; this definition of mortality has been widely used in research based on Taiwan’s NHI data.16-18

The second variable was physician continuity of care, which was measured as the COCI score.19 This index measures the distribution of clinical visits between physicians; a value between 0 and 1 was assigned to each patient. The closer the value was to 1, the higher the degree of continuity of care; a COCI score of 1 suggested that the same physician was seen by the patient at each outpatient visit. The index offered the advantage of being able to consider multiple physicians; the equation for the index is given below:

Mi=0   ni2N

N = number of medical visits per patient
ni = number of visits to physiciani per patient
M = total number of physicians visited
The independent variables consisted of 5 categories. The first was the personal characteristics of the research patients, including gender, age, and monthly salary. The second was the environmental factor. We used the degree of urbanization, categorized into 7 levels, to reflect the characteristics of the residential area. Level 1 indicated the highest degree of urbanization and level 7 represented the lowest.20 The third variable was health condition, including the severity of comorbidities and of the complications of diabetes. Severity of comorbidities was determined based on Deyo’s methodology, in which the primary and secondary ICD-9-CM diagnosis codes of the patients were converted into weighted numerical scores (using 1, 2, 3, and 6 points, respectively) and the weighted scores were summed to become the CCI score.14 The diabetes-related chronic complications of the CCI were not included in the CCI score to avoid duplication of the diseases already included in the DCSI score.13 The severity of complications of diabetes was determined according to the DCSI published by Young et al.15 This methodology includes the following complications of diabetes: diabetic retinopathy, nephropathy, neuropathy, cerebrovascular disease, cardiovascular diseases, peripheral vascular diseases, and endocrine complications. The primary and secondary ICD-9-CM diagnosis codes of these complications were then converted into numbers, which were summed and became the DCSI score (ranging from 0 [low] to 13 [high]). The fourth variable was characteristics of the primary physicians, represented by the physicians’ annual service volume: high (>75%), medium (25%-75%), and low (<25%), using the quartile method before being subjected to analysis. The fifth was characteristics of the primary healthcare organizations, including their level and ownership.

Data Analysis

SAS version 9.3 (SAS Institute, Cary, North Carolina) was used to process and analyze the data. The research patients were divided into those who had and had not participated in the P4P program. The differences in each independent variable of the 2 groups are presented using percentages, mean, and standard deviation. Moreover, the χ2 test was used to analyze whether there were significant differences in personal characteristics, health conditions, environmental factors, characteristics of primary physicians, and characteristics of major healthcare organizations between the 2 groups. Multiple regression analysis was used to examine the related factors of physician continuity of care in patients with diabetes. The Cox proportional hazard model was used to explore the related factors that affected the survival status of the patients with diabetes. This study was approved by the China Medical University Institutional Review Board.


A total of 396,838 subjects were included in this study, with 198,419 subjects in each group. After matching, all variables, including gender, age, monthly salary, CCI score, DCSI score, and level and ownership of healthcare organizations, revealed no significant differences between the 2 groups (Table 1).

We divided the patients into subgroups based on gender, age, monthly salary, level of residential urbanization, CCI score, DCSI score, physician’s annual service volume, level and ownership of healthcare organizations, and diabetes duration. In each subgroup, the mean COCI score of the P4P participants (0.57 to 0.67) was significantly higher than that of the nonparticipants (0.17 to 0.50), meaning that P4P participants had higher physician continuity. Detailed data are shown in Table 2.

Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up

Sign In

Not a member? Sign up now!