Comparison of Approaches for Aggregating Quality Measures in Population-based Payment Models

Mining voice of customers and employees in insurance companies from online reviews: a text analytics approach

Purpose

Although insurance companies contribute to about $600 billion of the US gross domestic product and employ 2.7 million people, the overall perception of clients and workers employed in this domain is poor. To the best of our knowledge, this study is the first to propose recommendations to improve the service quality of insurance companies using the voice of customers and employees that are available online.

Design/methodology/approach

The proposed methodology consists of four stages: extracting text reviews posted by employees and customers, reviewing feedback using bigrams and trigrams, determining topics and identifying quality-associated managerial recommendations using the Strengths, Weaknesses, Opportunities and Threats (SWOT) and root cause analysis techniques.

Findings

Our results indicate that primary causes for client dissatisfaction are improper client assistance services, inefficient claims processing, issues related to payments/fees and unresponsive ancillary services. Offering different types of payment methods, immediate car replacement policy and features such as smartphone applications are commended very well. The major reasons for workforce frustrations include long and overly complex training, lack of social events and incentive programs. Coworkers, workplace facilities, clean work environment and other amenities are shown to have a positive impact on employees' satisfaction level.

Originality/value

To the best of our knowledge, this study is the first to propose recommendations to improve the service quality of insurance companies using the voice of customers and employees that are available online. The proposed quality-related insights can assist insurance companies in improving the outlook of their workers as well as customers.

Using Consistently Low Performance to Identify Low-Quality Physician Groups

Importance

There has been a growth in the use of performance-based payment models in the past decade, but inherently noisy and stochastic quality measures complicate the assessment of the quality of physician groups. Examining consistently low performance across multiple measures or multiple years could potentially identify a subset of low-quality physician groups.

Objective

To identify low-performing physician groups based on consistently low performance after adjusting for patient characteristics across multiple measures or multiple years for 10 commonly used quality measures for diabetes and cardiovascular disease (CVD).

Design, Setting, and Participants

This cross-sectional study used medical and pharmacy claims and laboratory data for enrollees ages 18 to 65 years with diabetes or CVD in an Aetna health insurance plan between 2016 and 2019. Each physician group's risk-adjusted performance for a given year was estimated using mixed-effects linear probability regression models. Performance was correlated across measures and time, and the proportion of physician groups that performed in the bottom quartile was examined across multiple measures or multiple years. Data analysis was conducted between September 2020 and May 2021.

Exposures

Primary care physician groups.

Main Outcomes and Measures

Performance scores of 6 quality measures for diabetes and 4 for CVD, including hemoglobin A1c (HbA1c) testing, low-density lipoprotein testing, statin use, HbA1c control, low-density lipoprotein control, and hospital-based utilization.

Results

A total of 786 641 unique enrollees treated by 890 physician groups were included; 414 655 (52.7%) of the enrollees were men and the mean (SD) age was 53 (9.5) years. After adjusting for age, sex, and clinical and social risk variables, correlations among individual measures were weak (eg, performance-adjusted correlation between any statin use and LDL testing for patients with diabetes, r = -0.10) to moderate (correlation between LDL testing for diabetes and LDL testing for CVD, r = .43), but year-to-year correlations for all measures were moderate to strong. One percent or fewer of physician groups performed in the bottom quartile for all 6 diabetes measures or all 4 cardiovascular disease measures in any given year, while 14 (4.0%) to 39 groups (11.1%) were in the bottom quartile in all 4 years for any given measure other than hospital-based utilization for CVD (1.1%).

Conclusions and Relevance

A subset of physician groups that was consistently low performing could be identified by considering performance measures across multiple years. Considering the consistency of group performance could contribute a novel method to identify physician groups most likely to benefit from limited resources.

Social Risk Adjustment of Quality Measures for Diabetes and Cardiovascular Disease in a Commercially Insured US Population

IMPORTANCE

Patients' social risk factors may be associated with physician group performance on quality measures.

OBJECTIVE

To examine the association of social risk with change in physician group performance on diabetes and cardiovascular disease (CVD) quality measures in a commercially insured population.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study using claims data from 2010 to 2014 from a US national health insurance plan, the performance of 1400 physician groups (physicians billing under the same tax identification number) was estimated. After base adjustments for age and sex, changes in variation across groups and reordering of rankings resulting from additional adjustments for clinical, social, or both clinical and social risk factors were analyzed. In all models, only within-group associations were adjusted to distinguish the association of patients' social risk factors with outcomes while excluding physician groups' distinct characteristics that could also change observed performance. Data analysis was conducted between April and July 2018.

MAIN OUTCOMES AND MEASURES

Process measures (hemoglobin A1c [HbA1c] testing, low-density lipoprotein cholesterol [LDL-C] testing, and statin use), disease control measures (HbA1c and LDL-C level control), and use-based outcome measures (hospitalizations for ambulatory-sensitive conditions) were calculated with base adjustment (age and sex), clinical adjustment, social risk factor adjustment, and both clinical and social adjustments. Quality variance in physician group performance and changes in rankings following these adjustments were measured.

RESULTS

This study identified 1 684 167 enrollees (859 618 [51%] men) aged 18 to 65 years (mean [SD] age, 50 [10.7] years) with diabetes or CVD. Performance rates were high for HbA1c and LDL-C level testing (mean ranged from 79.5% to 87.2%) but lower for statin use (54.7% for diabetes cohort and 44.2% for CVD cohort) and disease control measures (57.9% on LDL-C control for diabetes cohort and 40.0% for CVD cohort). On average, only 8.8% of enrollees with diabetes and 1.0% of enrollees with CVD in a group were hospitalized. The addition of clinical and social risk factors to base adjustment reduced variance across physician groups for most measures (percentage change in SD ranged from -13.9% to 1.6%). Although overall agreement between performance scores with base vs full adjustment was high, there was still substantial reordering for some measures. For example, social risk adjustment resulted in reordering for disease control in the diabetes cohort. Of the 1400 physician groups, 330 (23.6%) had performance rankings for HbA1c control that increased or decreased by at least 10 percentile points after adding social risk factors to age and sex. Both clinical and social risk adjustment affected rankings on hospital admissions.

CONCLUSIONS AND RELEVANCE

Accounting for social risk may be important to mitigate adverse consequences of performance-based payments for physician groups serving socially vulnerable populations.