The impact of financial incentives to improve quality indicators in patients with diabetes in Swiss primary care: a protocol for a cluster randomised controlled trial

Impact of a diabetes disease management program on guideline-adherent care, hospitalization risk and health care costs: a propensity score matching study using real-world data

OBJECTIVE

To evaluate the impact of a DMP for patients with diabetes mellitus in a Swiss primary care setting.

METHODS

In a prospective observational study, we compared diabetes patients in a DMP (intervention group; N = 538) with diabetes patients receiving usual care (control group; N = 5050) using propensity score matching with entropy balancing. Using a difference-in-difference (DiD) approach, we compared changes in outcomes from baseline (2017) to 1-year (2017/18) and to 2-year follow-up (2017/19). Outcomes included four measures for guideline-adherent diabetes care, hospitalization risk, and health care costs.

RESULTS

We identified a positive impact of the DMP on the share of patients fulfilling all measures for guideline-adherent care [DiD 2017/18: 7.2 percentage-points, p < 0.01; 2017/19: 8.4 percentage-points, p < 0.001]. The hospitalization risk was lower in the intervention group in both years, but only statistically significant in the 1-year follow-up [DiD 2017/18: - 5.7 percentage-points, p < 0.05; 2017/19: - 3.9 percentage points, n.s.]. The increase in health care costs was smaller in the intervention than in the control group [DiD 2017/18: CHF - 852; 2017/19: CHF - 909], but this effect was not statistically significant.

CONCLUSION

The DMP under evaluation seems to exert a positive impact on the quality of diabetes care, reflected in the increase in the measures for guideline-adherent care and in a reduction of the hospitalization risk in the intervention group. It also might reduce health care costs, but only a longer follow-up will show whether the observed effect persists over time.

The Effect of Financial Incentives on Quality Measures in the Treatment of Diabetes Mellitus: a Randomized Controlled Trial

BACKGROUND

Financial incentives are often used to improve quality of care in chronic care patients. However, the evidence concerning the effect of financial incentives is still inconclusive.

OBJECTIVE

To test the effect of financial incentives on quality measures (QMs) in the treatment of patients with diabetes mellitus in primary care. We incentivized a clinical QM and a process QM to test the effect of financial incentives on different types of QMs and to investigate the spill-over effect on non-incentivized QMs.

DESIGN/PARTICIPANTS

Parallel cluster randomized controlled trial based on electronic medical records database involving Swiss general practitioners (GPs). Practices were randomly allocated.

INTERVENTION

All participants received a bimonthly feedback report. The intervention group additionally received potential financial incentives on GP level depending on their performance.

MAIN MEASURES

Between-group differences in proportions of patients fulfilling incentivized QM (process QM of annual HbA1c measurement and clinical QM of blood pressure level below 140/95 mmHg) after 12 months.

KEY RESULTS

Seventy-one GPs (median age 52 years, 72% male) from 43 different practices and subsequently 3838 patients with diabetes mellitus (median age 70 years, 57% male) were included. Proportions of patients with annual HbA1c measurements remained unchanged (intervention group decreased from 79.0 to 78.3%, control group from 81.5 to 81.0%, OR 1.09, 95% CI 0.90-1.32, p = 0.39). Proportions of patients with blood pressure below 140/95 improved from 49.9 to 52.5% in the intervention group and decreased from 51.2 to 49.0% in the control group (OR 1.16, 95% CI 0.99-1.36, p = 0.06). Proportions of non-incentivized process QMs increased significantly in the intervention group.

CONCLUSION

GP level financial incentives did not result in more frequent HbA1c measurements or in improved blood pressure control. Interestingly, we could confirm a spill-over effect on non-incentivized process QMs. Yet, the mechanism of spill-over effects of financial incentives is largely unclear.

TRIAL REGISTRATION

ISRCTN13305645.

Long-Term Effects of Financial Incentives for General Practitioners on Quality Indicators in the Treatment of Patients With Diabetes Mellitus in Primary Care-A Follow-Up Analysis of a Cluster Randomized Parallel Controlled Trial

Background: The effect of financial incentives on the quality of primary care is of high interest, and so is its sustainability after financial incentives are withdrawn.

Objective: To assess both long-term effects and sustainability of financial incentives for general practitioners (GPs) in the treatment of patients with diabetes mellitus based on quality indicators (QIs) calculated from routine data from electronic medical records.

Design/Participants: Randomized controlled trial using routine data from electronic medical records of patients with diabetes mellitus of Swiss GPs.

Intervention: During the study period of 24 months, all GPs received bimonthly feedback reports with information on their actual treatment as reflected in QIs. In the intervention group, the reports were combined with financial incentives for quality improvement. The incentive was stopped after 12 months.

Measurements: Proportion of patients meeting the process QI of annual HbA1c measurements and the clinical QI of blood pressure levels below 140/85 mmHg.

Results: A total of 71 GPs from 43 different practices were included along with 3,854 of their patients with diabetes mellitus. Throughout the study, the proportion of patients with annual HbA1c measurements was stable in the intervention group (78.8–78.9%) and decreased slightly in the control group (81.5–80.2%) [odds ratio (OR): 1.21; 95% CI: 1.04–1.42, p < 0.05]. The proportion of patients achieving blood pressure levels below 140/85 mmHg decreased in the control group (51.2–47.2%) and increased in the intervention group (49.7–51.9%) (OR: 1.18; 95% CI: 1.04–1.35, p < 0.05) where it peaked at 54.9% after 18 months and decreased steadily over the last 6 months.

Conclusion: After the withdrawal of financial incentives for the GPs after 12 months, some QIs still improved, indicating that 1 year might be too short to observe the full effect of such interventions. The decrease in QI achievement rates after 18 months suggests that the positive effects of time-limited financial incentives eventually wane.

Quality performance and associated factors in Swiss diabetes care - A cross-sectional study

INTRODUCTION

Quality indicators and pay-for-performance schemes aim to improve processes and outcomes in clinical practice. However, general practitioner and patient characteristics influence quality indicator performance. In Switzerland, no data on the pay-for-performance approach exists and the use of quality indicators has been marginal. The aim of this study was to describe quality indicator performance in diabetes care in Swiss primary care and to analyze associations of practice, general practitioner and patient covariates with quality indicator performance.

METHODS

For this cross-sectional study, we used medical routine data from an electronic medical record database. Data from 71 general practitioners and all their patients with diabetes were included. Starting in July 2018, we retrieved 12-month retrospective data about practice, general practitioner and patient characteristics, laboratory values, comorbidities and co-medication. Based on this data, we assessed quality indicator performance of process and intermediate outcomes for glycated hemoglobin, blood pressure, cholesterol and associations of practice, general practitioner and patient characteristics with individual and cumulative quality indicator performance. We calculated odds ratios (OR) and 95% confidence intervals (CI) using regression methods.

RESULTS

We assessed 3,383 patients with diabetes (57% male, mean age 68.3 years). On average, patients fulfilled 3.56 (standard deviation: 1.89) quality indicators, whereas 17.2% of the patients fulfilled all six quality indicators. On practice and general practitioner level, we found no associations with cumulative quality indicator performance. On patient level, gender (ref = male) (OR: 0.83, CI: 0.78-0.88), number of treating general practitioners (OR: 0.94, CI: 0.91-0.97), number of comorbidities (OR: 1.43, CI: 1.38-1.47) and number of consultations (OR: 1.02, CI: 1.02-1.02) were associated with cumulative quality indicator performance.

CONCLUSION

The influence of practice, general practitioner and patient characteristics on quality indicator performance was surprisingly small and room for improvement in quality indicator performance of Swiss general practitioners seems to exist in diabetes care.

Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA)

Objectives

This manuscript reviews the current state of veterinary medical electronic health records and the ability to aggregate and analyze large datasets from multiple organizations and clinics. We also review analytical techniques as well as research efforts into veterinary informatics with a focus on applications relevant to human and animal medicine. Our goal is to provide references and context for these resources so that researchers can identify resources of interest and translational opportunities to advance the field.

Methods and Results

This review covers various methods of veterinary informatics including natural language processing and machine learning techniques in brief and various ongoing and future projects. After detailing techniques and sources of data, we describe some of the challenges and opportunities within veterinary informatics as well as providing reviews of common One Health techniques and specific applications that affect both humans and animals.

Discussion

Current limitations in the field of veterinary informatics include limited sources of training data for developing machine learning and artificial intelligence algorithms, siloed data between academic institutions, corporate institutions, and many small private practices, and inconsistent data formats that make many integration problems difficult. Despite those limitations, there have been significant advancements in the field in the last few years and continued development of a few, key, large data resources that are available for interested clinicians and researchers. These real-world use cases and applications show current and significant future potential as veterinary informatics grows in importance. Veterinary informatics can forge new possibilities within veterinary medicine and between veterinary medicine, human medicine, and One Health initiatives.