Research-grade data in the real world: challenges and opportunities in data quality from a pragmatic trial in community-based practices

Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records

Aim: Catheter ablation is used to treat symptomatic atrial fibrillation (AF) and is performed using either cryoballoon (CB) or radiofrequency (RF) ablation. There is limited real world data of CB and RF in the US as healthcare codes are agnostic of energy modality. An alternative method is to analyze patients' electronic health records (EHRs) using Optum's EHR database. Objective: To determine the feasibility of using patients' EHRs with natural language processing (NLP) to distinguish CB versus RF ablation procedures. Data Source: Optum® de-identified EHR dataset, Optum® Cardiac Ablation NLP Table. Methods: This was a retrospective analysis of existing de-identified EHR data. Medical codes were used to create an ablation validation table. Frequency analysis was used to assess ablation procedures and their associated note terms. Two cohorts were created (1) index procedures, (2) multiple procedures. Possible note term combinations included (1) cryoablation (2) radiofrequency (3) ablation, or (4) both. Results: Of the 40,810 validated cardiac ablations, 3777 (9%) index ablation procedures had available and matching NLP note terms. Of these, 22% (n = 844) were classified as ablation, 27% (n = 1016) as cryoablation, 49% (n = 1855) as radiofrequency ablation, and 1.6% (n = 62) as both. In the multiple procedures analysis, 5691 (14%) procedures had matching note terms. 24% (n = 1362) were classified as ablation, 27% as cryoablation, 47% as radiofrequency ablation, and 2% as both. Conclusion: NLP has potential to evaluate the frequency of cardiac ablation by type, however, for this to be a reliable real-world data source, mandatory data entry by providers and standardized electronic health reporting must occur.

Integrating Community Health Workers into Community-Based Primary Care Practice Settings to Improve Blood Pressure Control Among South Asian Immigrants in New York City: Results from a Randomized Control Trial

BACKGROUND

Blood pressure (BP) control is suboptimal in minority communities, including Asian populations. We evaluate the feasibility, adoption, and effectiveness of an integrated CHW-led health coaching and practice-level intervention to improve hypertension control among South Asian patients in New York City, Project IMPACT (Integrating Million Hearts for Provider and Community Transformation). The primary outcome was BP control, and secondary outcomes were systolic BP and diastolic BP at 6-month follow-up.

METHODS

A randomized-controlled trial took place within community-based primary care practices that primarily serve South Asian patients in New York City between 2017 and 2019. A total of 303 South Asian patients aged 18-85 with diagnosed hypertension and uncontrolled BP (systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg) within the previous 6 months at 14 clinic sites consented to participate. After completing 1 education session, individuals were randomized into treatment (n=159) or control (n=144) groups. Treatment participants received 4 additional group education sessions and individualized health coaching over a 6-month period. A mixed effect generalized linear model with a logit link function was used to assess intervention effectiveness for controlled hypertension (Yes/No), adjusting for practice level random effect, age, sex, baseline systolic BP, and days between BP measurements.

RESULTS

Among the total enrolled population, mean age was 56.8±11.2 years, and 54.1% were women. At 6 months among individuals with follow-up BP data (treatment, n=154; control, n=137), 68.2% of the treatment group and 41.6% of the control group had controlled BP (P<0.001). In final adjusted analysis, treatment group participants had 3.7 [95% CI, 2.1-6.5] times the odds of achieving BP control at follow-up compared with the control group.

CONCLUSIONS

A CHW-led health coaching intervention was effective in achieving BP control among South Asian Americans in New York City primary care practices. Findings can guide translation and dissemination of this model across other communities experiencing hypertension disparities.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT03159533.

Performance of EHR classifiers for patient eligibility in a clinical trial of precision screening

BACKGROUND

Validated computable eligibility criteria use real-world data and facilitate the conduct of clinical trials. The Genomic Medicine at VA (GenoVA) Study is a pragmatic trial of polygenic risk score testing enrolling patients without known diagnoses of 6 common diseases: atrial fibrillation, coronary artery disease, type 2 diabetes, breast cancer, colorectal cancer, and prostate cancer. We describe the validation of computable disease classifiers as eligibility criteria and their performance in the first 16 months of trial enrollment.

METHODS

We identified well-performing published computable classifiers for the 6 target diseases and validated these in the target population using blinded physician review. If needed, classifiers were refined and then underwent a subsequent round of blinded review until true positive and true negative rates ≥80% were achieved. The optimized classifiers were then implemented as pre-screening exclusion criteria; telephone screens enabled an assessment of their real-world negative predictive value (NPV-RW).

RESULTS

Published classifiers for type 2 diabetes and breast and prostate cancer achieved desired performance in blinded chart review without modification; the classifier for atrial fibrillation required two rounds of refinement before achieving desired performance. Among the 1077 potential participants screened in the first 16 months of enrollment, NPV-RW of the classifiers ranged from 98.4% for coronary artery disease to 99.9% for colorectal cancer. Performance did not differ by gender or race/ethnicity.

CONCLUSIONS

Computable disease classifiers can serve as efficient and accurate pre-screening classifiers for clinical trials, although performance will depend on the trial objectives and diseases under study.

Contemporary use of real-world data for clinical trial conduct in the United States: a scoping review

OBJECTIVE

Real-world data (RWD), defined as routinely collected healthcare data, can be a potential catalyst for addressing challenges faced in clinical trials. We performed a scoping review of database-specific RWD applications within clinical trial contexts, synthesizing prominent uses and themes.

MATERIALS AND METHODS

Querying 3 biomedical literature databases, research articles using electronic health records, administrative claims databases, or clinical registries either within a clinical trial or in tandem with methodology related to clinical trials were included. Articles were required to use at least 1 US RWD source. All abstract screening, full-text screening, and data extraction was performed by 1 reviewer. Two reviewers independently verified all decisions.

RESULTS

Of 2020 screened articles, 89 qualified: 59 articles used electronic health records, 29 used administrative claims, and 26 used registries. Our synthesis was driven by the general life cycle of a clinical trial, culminating into 3 major themes: trial process tasks (51 articles); dissemination strategies (6); and generalizability assessments (34). Despite a diverse set of diseases studied, <10% of trials using RWD for trial process tasks evaluated medications or procedures (5/51). All articles highlighted data-related challenges, such as missing values.

DISCUSSION

Database-specific RWD have been occasionally leveraged for various clinical trial tasks. We observed underuse of RWD within conducted medication or procedure trials, though it is subject to the confounder of implicit report of RWD use.

CONCLUSION

Enhanced incorporation of RWD should be further explored for medication or procedure trials, including better understanding of how to handle related data quality issues to facilitate RWD use.

The Role of Health Informatics in Facilitating Communication Strategies for Community Health Workers in Clinical Settings: A Scoping Review

BACKGROUND

Community health workers (CHWs) have been identified as effective members of health care teams in improving health outcomes and reducing health disparities, especially among racial and ethnic minorities. There is a growing interest in integrating CHWs into clinical settings using health informatics-based strategies to help provide coordinated patient care and foster health-promoting behaviors.

OBJECTIVE

In this scoping review, we outline health informatics-based strategies for CHW-provider communication that aim to improve integration of CHWs into clinical settings.

DESIGN

A scoping review was conducted.

ELIGIBILITY CRITERIA

US-based sources between 2013 and 2018 were eligible.

STUDY SELECTION

Literature was identified through PubMed and Google queries and hand searching key reference lists. Articles were screened by title, abstract, and then full-text.

MAIN OUTCOME MEASURES

Health informatics-based strategies for CHW-provider communication and their impacts on patient care were documented and analyzed.

RESULTS

Thirty-one articles discussed health informatics-based strategies for CHW-provider communication and/or integration of CHWs into clinical settings. These strategies include direct CHW documentation of patient encounters in electronic health records (EHRs) and other Web-based applications. The technologies were used to document patient encounters and patient barriers to health care providers but were additionally used for secure messaging and referral systems. These strategies were found to meet the needs of providers and CHWs while facilitating CHW-provider communication, CHW integration, and coordinated care.

CONCLUSIONS

Health informatics-based strategies for CHW-provider communication are important for facilitating CHW integration and potentially improving patient outcomes and improving disparities among minority populations. This integration can support the development of future disease prevention programs and health care policies in which CHWs are an established part of the public health workforce. However, further investigation must be done on overcoming implementation challenges (eg, lack of time or funding), especially in smaller resource-challenged community-based clinics that serve minority patients.

Using UMLS for electronic health data standardization and database design

OBJECTIVE

Patients that undergo medical transfer represent 1 patient population that remains infrequently studied due to challenges in aggregating data across multiple domains and sources that are necessary to capture the entire episode of patient care. To facilitate access to and secondary use of transport patient data, we developed the Transport Data Repository that combines data from 3 separate domains and many sources within our health system.

METHODS

The repository is a relational database anchored by the Unified Medical Language System unique concept identifiers to integrate, map, and standardize the data into a common data model. Primary data domains included sending and receiving hospital encounters, medical transport record, and custom hospital transport log data. A 4-step mapping process was developed: 1) automatic source code match, 2) exact text match, 3) fuzzy matching, and 4) manual matching.

RESULTS

431 090 total mappings were generated in the Transport Data Repository, consisting of 69 010 unique concepts with 77% of the data being mapped automatically. Transport Source Data yielded significantly lower mapping results with only 8% of data entities automatically mapped and a significant amount (43%) remaining unmapped.

DISCUSSION

The multistep mapping process resulted in a majority of data been automatically mapped. Poor matching of transport medical record data is due to the third-party vendor data being generated and stored in a nonstandardized format.

CONCLUSION

The multistep mapping process developed and implemented is necessary to normalize electronic health data from multiple domains and sources into a common data model to support secondary use of data.

Health informatics and health equity: improving our reach and impact

Health informatics studies the use of information technology to improve human health. As informaticists, we seek to reduce the gaps between current healthcare practices and our societal goals for better health and healthcare quality, safety, or cost. It is time to recognize health equity as one of these societal goals-a point underscored by this Journal of the American Medical Informatics Association Special Focus Issue, "Health Informatics and Health Equity: Improving our Reach and Impact." This Special Issue highlights health informatics research that focuses on marginalized and underserved groups, health disparities, and health equity. In particular, this Special Issue intentionally showcases high-quality research and professional experiences that encompass a broad range of subdisciplines, methods, marginalized populations, and approaches to disparities. Building on this variety of submissions and other recent developments, we highlight contents of the Special Issue and offer an assessment of the state of research at the intersection of health informatics and health equity.

Small Practices, Big (QI) Dreams: Customizing QI Efforts for Under-resourced Primary Care Practices to Improve Diabetes Disparities (Preprint)

Electronic health record quality improvement (QI) initiatives hold great promise in improving adoption of clinical practice guidelines, including those related to diabetes. QI initiatives implemented in under-resourced primary care settings that primarily serve racial/ethnic minority populations have potential to improve quality of care and ultimately improve diabetes disparities. The “Screen at 23” campaign was launched in 2011 to increase screening for prediabetes and diabetes at lower BMI thresholds (ie, 23 kg/m2) for Asian Americans, in line with the new guidelines put forth by the American Diabetes Association. Here, we describe the implementation of a customized electronic health record QI initiative in under-resourced practices that primarily serve low-income South Asian populations in New York City, designed to increase diabetes screening using updated BMI guidelines and in alignment with the “Screen at 23” campaign. The customization involved the implementation of an innovative, semi-manual alternate solution to automated clinical decision support system (CDSS) alerts in order to address the restrictions on customizing CDSS alerts in electronic health record platforms used in small practice settings. We also discuss challenges and strategies with this customized QI effort. Our experience suggests that multisector partnership engagement, user-centered approaches, and informal strategies for relationship building are even more critical in under-resourced, small practice settings. Relatively simple technological solutions can be greatly beneficial in enhancing small practice capacity to engage in larger-scale QI initiatives. Tailored, context-driven approaches for implementation of equity-focused QI initiatives such as the one we describe can increase adoption of clinical practice guidelines, improve diabetes-related outcomes, and improve health disparities among underserved populations.