Association of Peer Comparison Emails With Electronic Health Record Documentation of Cancer Stage by Oncologists

Data Quality-Driven Improvement in Health Care: Systematic Literature Review

BACKGROUND

The promise of real-world evidence and the learning health care system primarily depends on access to high-quality data. Despite widespread awareness of the prevalence and potential impacts of poor data quality (DQ), best practices for its assessment and improvement are unknown.

OBJECTIVE

This review aims to investigate how existing research studies define, assess, and improve the quality of structured real-world health care data.

METHODS

A systematic literature search of studies in the English language was implemented in the Embase and PubMed databases to select studies that specifically aimed to measure and improve the quality of structured real-world data within any clinical setting. The time frame for the analysis was from January 1945 to June 2023. We standardized DQ concepts according to the Data Management Association (DAMA) DQ framework to enable comparison between studies. After screening and filtering by 2 independent authors, we identified 39 relevant articles reporting DQ improvement initiatives.

RESULTS

The studies were characterized by considerable heterogeneity in settings and approaches to DQ assessment and improvement. Affiliated institutions were from 18 different countries and 18 different health domains. DQ assessment methods were largely manual and targeted completeness and 1 other DQ dimension. Use of DQ frameworks was limited to the Weiskopf and Weng (3/6, 50%) or Kahn harmonized model (3/6, 50%). Use of standardized methodologies to design and implement quality improvement was lacking, but mainly included plan-do-study-act (PDSA) or define-measure-analyze-improve-control (DMAIC) cycles. Most studies reported DQ improvements using multiple interventions, which included either DQ reporting and personalized feedback (24/39, 61%), IT-related solutions (21/39, 54%), training (17/39, 44%), improvements in workflows (5/39, 13%), or data cleaning (3/39, 8%). Most studies reported improvements in DQ through a combination of these interventions. Statistical methods were used to determine significance of treatment effect (22/39, 56% times), but only 1 study implemented a randomized controlled study design. Variability in study designs, approaches to delivering interventions, and reporting DQ changes hindered a robust meta-analysis of treatment effects.

CONCLUSIONS

There is an urgent need for standardized guidelines in DQ improvement research to enable comparison and effective synthesis of lessons learned. Frameworks such as PDSA learning cycles and the DAMA DQ framework can facilitate this unmet need. In addition, DQ improvement studies can also benefit from prioritizing root cause analysis of DQ issues to ensure the most appropriate intervention is implemented, thereby ensuring long-term, sustainable improvement. Despite the rise in DQ improvement studies in the last decade, significant heterogeneity in methodologies and reporting remains a challenge. Adopting standardized frameworks for DQ assessment, analysis, and improvement can enhance the effectiveness, comparability, and generalizability of DQ improvement initiatives.

Registry-derived stage (RD-Stage) for capturing cancer stage at diagnosis for endometrial cancer

BACKGROUND

Capture of cancer stage at diagnosis is important yet poorly reported by health services to population-based cancer registries. In this paper we describe current completeness of stage information for endometrial cancer available in Australian cancer registries; and develop and validate a set of rules to enable cancer registry medical coders to calculate stage using data available to them (registry-derived stage or 'RD-Stage').

METHODOLOGY

Rules for deriving RD-stage (Endometrial carcinoma) were developed using the American Joint Commission on Cancer (AJCC) TNM (tumour, nodes, metastasis) Staging System (8th Edition). An expert working group comprising cancer specialists responsible for delivering cancer care, epidemiologists and medical coders reviewed and endorsed the rules. Baseline completeness of data fields required to calculate RD-Stage, and calculation of the proportion of cases for whom an RD stage could be assigned, was assessed across each Australian jurisdiction. RD-Stage (Endometrial cancer) was calculated by Victorian Cancer Registry (VCR) medical coders and compared with clinical stage recorded by the patient's treating clinician and captured in the National Gynae-Oncology Registry (NGOR).

RESULTS

The necessary data completeness level for calculating RD-Stage (Endometrial carcinoma) across various Australian jurisdictions varied from 0 to 89%. Three jurisdictions captured degree of spread of cancer, rendering RD-Stage unable to be calculated. RD-Stage (Endometrial carcinoma) could not be derived for 64/485 (13%) cases and was not captured for 44/485 (9%) cases in NGOR. At stage category level (I, II, III, IV), there was concordance between RD-Stage and NGOR captured stage in 393/410 (96%) of cases (95.8%, Kendall's coefficient = 0.95).

CONCLUSION

A lack of consistency in data captured by, and data sources reporting to, population-based cancer registries meant that it was not possible to provide national endometrial carcinoma stage data at diagnosis. In a sample of Victorian cases, where surgical pathology was available, there was very good concordance between RD-Stage (Endometrial carcinoma) and clinician-recorded stage data available from NGOR. RD-Stage offers promise in capturing endometrial cancer stage at diagnosis for population epidemiological purposes when it is not provided by health services, but requires more extensive validation.

Creating a culture for change: Lessons from behavioral economics and complexity science to increase serious illness conversations for patients with cancer

Patient-centered cancer care requires communication between patients and clinicians about patients' goals, values, and preferences. Serious illness communication improves patient and caregiver outcomes, the value and quality of cancer care, and the well-being of clinicians. Despite these benefits, there are competing factors including time, capacity, bandwidth, and resistance. Health systems and oncology practices have opportunities to invest in pathways that assist patients and clinicians to engage in serious illness conversations. We discuss how applying insights from behavioral economics and complexity science may help clinicians engage in serious illness conversation and improve patient-centered cancer care.

Supporting Structured Data Capture for Patients With Cancer: An Initiative of the University of Wisconsin Carbone Cancer Center Survivorship Program to Improve Capture of Malignant Diagnosis and Cancer Staging Data

PURPOSE

Structured data elements within electronic health records are health-related information that can be entered, stored, and extracted in an organized manner at later time points. Tracking outcomes for cancer survivors is also enabled by structured data. We sought to increase structured data capture within oncology practices at multiple sites sharing the same electronic health records.

METHODS

Applying engineering approaches and the Plan-Do-Study-Act cycle, we launched dual quality improvement initiatives to ensure that a malignant diagnosis and stage were captured as structured data. Intervention: Close Visit Validation (CVV) requires providers to satisfy certain criteria before closing ambulatory encounters. CVV may be used to track open clinical encounters and chart delinquencies to encourage optimal clinical workflows. We added two cancer-specific required criteria at the time of closing encounters in oncology clinics: (1) the presence of at least one malignant diagnosis on the Problem List and (2) staging all the malignant diagnoses on the Problem List when appropriate.

RESULTS

Six months before the CVV implementation, the percentage of encounters with a malignant diagnosis on the Problem List at the time of the encounter was 65%, whereas the percentage of encounters with a staged diagnosis was 32%. Three months after cancer-specific CVV implementation, the percentages were 85% and 75%, respectively. Rates had increased to 90% and 88% more than 2 years after implementation.

CONCLUSION

Oncologist performance improved after the implementation of cancer-specific CVV criteria, with persistently high percentages of relevant malignant diagnoses and cancer stage structured data capture 2 years after the intervention.

Oncologist Peer Comparisons as a Behavioral Science Strategy to Improve Hospice Utilization

PURPOSE

Hospice utilization metrics are essential for any serious effort to improve end-of-life care in oncology. However, oncologists do not routinely receive these personalized reports. We evaluated whether a behavioral science intervention, using peer comparisons coupled with social norms, was associated with improvements in hospice use.

METHODS

Oncologists at two academic practices of Johns Hopkins Medicine were randomly assigned to receive a peer comparison report by e-mail displaying individual hospice utilization metrics compared with top-performing peers or to receive no report. The data accrued for the intervention represented hospice utilization for the previous calendar year. The intervention period was from June 1, 2020, to December 30, 2020, and included oncologists from both the solid and hematologic malignancies programs. The primary outcome was the proportion of patients between groups with short hospice length of stay (LOS; defined as ≤ 7 days) after 6 months. Secondary outcomes included hospice referral rate, enrollment rate, and median LOS.

RESULTS

Forty-seven oncologists participated. The percent of patients with a short hospice stay in the intervention group was lower (17.4%) compared with patients treated by physicians in the usual care group (46.3%, difference = 21.8%; 95% CI, 16.0 to 41.6; P < .001). Receipt of peer comparisons was associated with a greater likelihood of enrolling in hospice (73.7% v 42.8%; difference = 31.1%; 95% CI, 20.4 to 41.7; P < .001) and a longer hospice LOS (37.2 v 18.3 days; difference = 17.2; 95% CI, 8.8 to 25.7 days; P < .001).

CONCLUSION

Peer comparisons improved hospice utilization metrics among a group of academic oncologists. Behavioral science offers one pragmatic strategy to overcome suboptimal oncologist decision-making biases related to hospice use.

Impact on hospital-wide antipsychotic prescribing practices through physician peer comparison letters

Peer comparison is a behavioral strategy that provides feedback to individuals on how they compare with others. It is used to improve health care quality, reduce inappropriate prescribing, and improve physician performance. There is very little data on peer comparison and the impact on system-wide prescribing practices, particularly with antipsychotics. To that end, the Maryland statewide pharmacy and therapeutics committee reviews hospital-level antipsychotic data for 5 facilities on a quarterly basis, including high doses and polypharmacy. One facility, Springfield Hospital Center, consistently stood out in 2016 as having higher rates of high doses of haloperidol, olanzapine, and quetiapine as well as patients receiving 3 or more antipsychotics. The pharmacist began to send out individual letters to the psychiatrists detailing their prescribing habits in these areas compared with other psychiatrists and the other state facilities. Over the course of 4 years, the percentage of patients on high doses of 3 antipsychotics substantially decreased. The percentage of patients on polypharmacy in the facility decreased, but not at the same rate as the other hospitals, leaving the facility even higher than the state average at the end of the 4-year period. Pharmacist-initiated physician peer comparison letters were associated with a considerable decrease in the prevalence of high-dose olanzapine, haloperidol, and quetiapine but did not appear to impact antipsychotic polypharmacy. This type of communication may be beneficial for stimulating system-wide changes in prescribing practices for high doses of antipsychotics; however, more individualized interventions are likely needed to reduce antipsychotic polypharmacy.

Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality

PURPOSE

The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed.

MATERIALS AND METHODS

Using the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates). At the time of this analysis, the CancerLinQ network comprised 63 active practices, representing eight different EHR vendors and containing records for more than 1.63 million unique patients with one or more malignant neoplasms (1.73 million cancer cases).

RESULTS

Fill rates for the 63 oMIPS-associated DEs varied widely among the practices. The average site had at least one filled DE for 52% of the DEs. Only 35% of the DEs were populated for at least one patient record in 95% of the practices. However, the average DE fill rate of all practices was 23%. No data were found at any practice for 22% of the DEs. Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, only two (10.5%) of the 19 oMIPS CQMs were computable for more than 1% of the patients.

CONCLUSION

Although EHR systems had relatively high DE fill rates for some DEs, underfilling and inconsistency of DEs in EHRs render automated oncology MIPS CQM calculations impractical.