Targeted Supplemental Data Collection - Addressing the Quality-Measurement Conundrum

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.

Evaluation of Reliability and Correlations of Quality Measures in Cancer Care

IMPORTANCE

Measurement of the quality of care is important for alternative payment models in oncology, yet the ability to distinguish high-quality from low-quality care across oncology practices remains uncertain.

OBJECTIVE

To assess the reliability of cancer care quality measures across oncology practices using registry and claims-based measures of process, utilization, end-of-life (EOL) care, and survival, and to assess the correlations of practice-level performance across measure and cancer types.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used the Surveillance, Epidemiology, and End Results (SEER) Program registry linked to Medicare administrative data to identify individuals with lung cancer, breast cancer, or colorectal cancer (CRC) that was newly diagnosed between January 1, 2011, and December 31, 2015, and who were treated in oncology practices with 20 or more patients. Data were analyzed from January 2018 to December 2020.

MAIN OUTCOMES AND MEASURES

Receipt of guideline-recommended treatment and surveillance, hospitalizations or emergency department visits during 6-month chemotherapy episodes, care intensity in the last month of life, and 12-month survival were measured. Summary measures for each domain in each cohort were calculated. Practice-level rates for each measure were estimated from hierarchical linear models with practice-level random effects; practice-level reliability (reproducibility) for each measure based on the between-measure variance, within-measure variance, and distribution of patients treated in each practice; and correlations of measures across measure and cancer types.

RESULTS

In this study of SEER registry data linked to Medicare administrative data from 49 715 patients with lung cancer treated in 502 oncology practices, 21 692 with CRC treated in 347 practices, and 52 901 with breast cancer treated in 492 practices, few practices had 20 or more patients who were eligible for most process measures during the 5-year study period. Patients were 65 years or older; approximately 50% of the patients with lung cancer and CRC and all of the patients with breast cancer were women. Most measures had limited variability across practices. Among process measures, 0 of 6 for lung cancer, 0 of 6 for CRC, and 3 of 11 for breast cancer had a practice-level reliability of 0.75 or higher for the median-sized practice. No utilization, EOL care, or survival measure had reliability across practices of 0.75 or higher. Correlations across measure types were low (r ≤ 0.20 for all) except for a correlation between the CRC process and 1-year survival summary measures (r = 0.35; P < .001). Summary process measures had limited or no correlation across lung cancer, breast cancer, and CRC (r ≤ 0.16 for all).

CONCLUSIONS AND RELEVANCE

This study found that quality measures were limited by the small numbers of Medicare patients with newly diagnosed cancer treated in oncology practices, even after pooling 5 years of data. Measures had low reliability and had limited to no correlation across measure and cancer types, suggesting the need for research to identify reliable quality measures for practice-level quality assessments.

The Mayo Clinic Hospital Mortality Reduction Project: Description and Results

EXECUTIVE SUMMARY

Quality improvement, regulatory, and payer organizations use various definitions of hospital mortality as clinical outcome measures. In this prospective study, the authors evaluated a multicomponent intervention aimed at reducing inpatient mortality in a multistate healthcare delivery system. The project was initiated because of a statistically nonsignificant upward trend in mortality suggested by a six-quarter rise in the observed/expected mortality ratio generated by the Vizient Clinical Data Base and Resource Manager. The design of the mortality reduction plan was influenced by the known limitations of using hospital-wide mortality as a quality improvement measure. The primary objective was to reduce mortality through focused care redesign. The project leadership team attempted to implement standardized system-wide improvements while allowing individual hospitals to simultaneously pursue site-specific practice redesign opportunities. Between Q3, 2015, and Q4, 2017, system-wide mortality reduced from 1.78 to 1.53 (per 100 admissions; p = .01). The actual plan implemented in Mayo Clinic's hospitals is included as Appendix A to this article, published online as Supplemental Digital Content. The authors included it to allow comparison with similar efforts at other healthcare systems, as well as to stimulate criticism and discussion by readers.

Professionalism Revealed: Rethinking Quality Improvement in the Wake of a Pandemic

The pace of health care quality improvement in the United States has been slow. After 2 decades of efforts relying largely on quality measurement and performance-linked payment incentives, we need new ideas and new conversations. As revealed by health care workers’ response to the Covid-19 pandemic, professionalism in health care may be an underused resource. Reframing quality improvement around the linchpin of care delivery — physician agency — could provide much-needed direction by elucidating strategies that address problems of information or motivation when professionals act as agents on their patients’ behalf. These strategies need not rely on measures. Physicians’ collective ability to observe and learn can be better tapped and their intrinsic motivation better supported. This article discusses the inherent limitations of measure-focused approaches, provides a framework for conceiving a next generation of initiatives that aim to improve care by more productively leveraging professionalism, and offers specific directions for policy and practice.

Mobile health ethics and the expanding role of autonomy

Mhealth technology is mushrooming world-wide and, in a variety of forms, reaches increasing numbers of users in ever-widening contexts and virtually independent from standard medical evidence assessment. Yet, debate on the broader societal impact including in particular mapping and classification of ethical issues raised has been limited. This article, as part of an ongoing empirically informed ethical research project, provides an overview of ethical issues of mhealth applications with a specific focus on implications on autonomy as a key notion in the debate. A multi-stage model of references to the potential of mhealth use for strengthening some or other form of self-determination will be proposed as a descriptive tool. It illustrates an assumed continuum of enhanced autonomy via mhealth broadly conceived: from patient to user autonomy, to improved health literacy, and finally to the vision of supra-individual empowerment and democratised, participatory health and medicine as a whole. On closer examination, however, these references are frequently ambivalent or vague, perpetuating the at times uncritical use of established autonomy concepts in medical ethics. The article suggests zooming in on the range of autonomy-related aspects against the backdrop of digital innovation and datafied health more generally, and on this basis add to existing frameworks for the ethical evaluation of mhealth more specifically.

Days at Home after Surgery: An Integrated and Efficient Outcome Measure for Clinical Trials and Quality Assurance

BACKGROUND

Surgical audit, sometimes including public reporting, is an important foundation of high quality health care. We aimed to assess the validity of a novel outcome metric, days at home up to 30 days after surgery, as a surgical outcome measure in clinical trials and quality assurance.

METHODS

This was a multicentre, registry-based cohort study. We used prospectively collected hospital and national healthcare registry data obtained from patients aged 18 years or older undergoing a broad range of surgeries in Sweden over a 10-year period. The association between days at home up to 30 days after surgery and patient (older age, poorer physical status, comorbidity) and surgical (elective or non-elective, complexity, duration) risk factors, process of care outcomes (re-admissions, discharge destination), clinical outcomes (major complications, 30-day mortality) and death up to 1 year after surgery were measured.

FINDINGS

From January, 2005, to December, 2014, we obtained demographic and perioperative data on 636,885 patients from 21 Swedish hospitals. Mortality at 30 days and one year was 1.8% and 7.3%, respectively. The median (IQR) days at home up to 30 days after surgery was 27 (23-29), being significantly lower among high-risk patients, those recovering from more complex surgical procedures, and suffering serious postoperative complications (all p < 0.0001). Patients with 8 days or less at home up to 30 days after surgery had a nearly 7-fold higher risk of death up to 1 year postoperatively when compared with those with 29 or 30 days at home (adjusted HR 6.78 [95% CI: 6.44-7.13]).

INTERPRETATION

Days at home up to 30 days after surgery is a valid, easy to measure patient-centred outcome metric. It is highly sensitive to changes in surgical risk and impact of complications, and has prognostic importance; it is therefore a valuable endpoint for perioperative clinical trials and quality assurance.

FUNDING

Swedish National Research Council Medicine and Stockholm County Council ALF-project grant (LE), and the Australian National Health and Medical Research Council (PM).