Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR

MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance

Objectives

The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline.

Materials and Methods

The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database.

Results

Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed.

Discussion

OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data.

Conclusion

MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.

MENDS-on-FHIR: Leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance

Objective

The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline.

Materials and Methods

The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database.

Results

Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed.

Discussion

OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data.

Conclusion

MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.

Playing in the clinical decision support sandbox: tools and training for all

Objectives

Introduce the CDS-Sandbox, a cloud-based virtual machine created to facilitate Clinical Decision Support (CDS) developers and implementers in the use of FHIR- and CQL-based open-source tools and technologies for building and testing CDS artifacts.

Materials and Methods

The CDS-Sandbox includes components that enable workflows for authoring and testing CDS artifacts. Two workshops at the 2020 and 2021 AMIA Annual Symposia were conducted to demonstrate the use of the open-source CDS tools.

Results

The CDS-Sandbox successfully integrated the use of open-source CDS tools. Both workshops were well attended. Participants demonstrated use and understanding of the workshop materials and provided positive feedback after the workshops.

Discussion

The CDS-Sandbox and publicly available tutorial materials facilitated an understanding of the leading-edge open-source CDS infrastructure components.

Conclusion

The CDS-Sandbox supports integrated use of the key CDS open-source tools that may be used to introduce CDS concepts and practice to the clinical informatics community.

An Ostensive Information Architecture to Enhance Semantic Interoperability for Healthcare Information Systems

Semantic interoperability establishes intercommunications and enables data sharing across disparate systems. In this study, we propose an ostensive information architecture for healthcare information systems to decrease ambiguity caused by using signs in different contexts for different purposes. The ostensive information architecture adopts a consensus-based approach initiated from the perspective of information systems re-design and can be applied to other domains where information exchange is required between heterogeneous systems. Driven by the issues in FHIR (Fast Health Interoperability Resources) implementation, an ostensive approach that supplements the current lexical approach in semantic exchange is proposed. A Semantic Engine with an FHIR knowledge graph as the core is constructed using Neo4j to provide semantic interpretation and examples. The MIMIC III (Medical Information Mart for Intensive Care) datasets and diabetes datasets have been employed to demonstrate the effectiveness of the proposed information architecture. We further discuss the benefits of the separation of semantic interpretation and data storage from the perspective of information system design, and the semantic reasoning towards patient-centric care underpinned by the Semantic Engine.

Merit-based incentive payment system participation and after-hours documentation among US office-based physicians: Findings from the 2021 National Electronic Health Records Survey

BACKGROUND

After-hours documentation burden among US clinicians is often uncompensated work and has been associated with burnout, leading health systems to identify root causes and seek interventions to reduce this. A few studies have suggested quality programme participation (e.g., Merit-Based Incentive Payment System [MIPS]) was associated with a higher administrative burden. However, the association between MIPS participation and after-hours documentation has not been fully explored. Thus, this study aims to assess whether participation in the MIPS programme was independently associated with after-hours documentation burden.

METHODS

We used 2021 data from the National Electronic Health Records Survey. We used a multivariable ordinal logistic regression model to assess whether MIPS participation was associated with the amount of after-hours documentation burden when controlling for other factors. We controlled for physician age, specialty, sex, number of practice locations, number of physicians, practice ownership, whether team support (e.g., scribes) is used for documentation tasks, and whether the practice accepts Medicaid patients.

RESULTS

We included 1801 office-based US physician respondents with complete data for variables of interest. After controlling for other factors, MIPS participation was associated with greater odds of spending a greater number of hours on after-hours documentation (odds ratio = 1.44, 95% confidence interval 1.06-1.95).

CONCLUSIONS

MIPS participation may increase after-hours documentation burden among US office-based physicians, suggesting that physicians may require additional resources to more efficiently report data.

"fhircrackr": An R Package Unlocking Fast Healthcare Interoperability Resources for Statistical Analysis

BACKGROUND

The growing interest in the secondary use of electronic health record (EHR) data has increased the number of new data integration and data sharing infrastructures. The present work has been developed in the context of the German Medical Informatics Initiative, where 29 university hospitals agreed to the usage of the Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard for their newly established data integration centers. This standard is optimized to describe and exchange medical data but less suitable for standard statistical analysis which mostly requires tabular data formats.

OBJECTIVES

The objective of this work is to establish a tool that makes FHIR data accessible for standard statistical analysis by providing means to retrieve and transform data from a FHIR server. The tool should be implemented in a programming environment known to most data analysts and offer functions with variable degrees of flexibility and automation catering to users with different levels of FHIR expertise.

METHODS

We propose the fhircrackr framework, which allows downloading and flattening FHIR resources for data analysis. The framework supports different download and authentication protocols and gives the user full control over the data that is extracted from the FHIR resources and transformed into tables. We implemented it using the programming language R [1] and published it under the GPL-3 open source license.

RESULTS

The framework was successfully applied to both publicly available test data and real-world data from several ongoing studies. While the processing of larger real-world data sets puts a considerable burden on computation time and memory consumption, those challenges can be attenuated with a number of suitable measures like parallelization and temporary storage mechanisms.

CONCLUSION

The fhircrackr R package provides an open source solution within an environment that is familiar to most data scientists and helps overcome the practical challenges that still hamper the usage of EHR data for research.

A Scalable Service to Improve Health Care Quality Through Precision Audit and Feedback: Proposal for a Randomized Controlled Trial

BACKGROUND

Health care delivery organizations lack evidence-based strategies for using quality measurement data to improve performance. Audit and feedback (A&F), the delivery of clinical performance summaries to providers, demonstrates the potential for large effects on clinical practice but is currently implemented as a blunt one size fits most intervention. Each provider in a care setting typically receives a performance summary of identical metrics in a common format despite the growing recognition that precisionizing interventions hold significant promise in improving their impact. A precision approach to A&F prioritizes the display of information in a single metric that, for each recipient, carries the highest value for performance improvement, such as when the metric's level drops below a peer benchmark or minimum standard for the first time, thereby revealing an actionable performance gap. Furthermore, precision A&F uses an optimal message format (including framing and visual displays) based on what is known about the recipient and the intended gist meaning being communicated to improve message interpretation while reducing the cognitive processing burden. Well-established psychological principles, frameworks, and theories form a feedback intervention knowledge base to achieve precision A&F. From an informatics perspective, precision A&F requires a knowledge-based system that enables mass customization by representing knowledge configurable at the group and individual levels.

OBJECTIVE

This study aims to implement and evaluate a demonstration system for precision A&F in anesthesia care and to assess the effect of precision feedback emails on care quality and outcomes in a national quality improvement consortium.

METHODS

We propose to achieve our aims by conducting 3 studies: a requirements analysis and preferences elicitation study using human-centered design and conjoint analysis methods, a software service development and implementation study, and a cluster randomized controlled trial of a precision A&F service with a concurrent process evaluation. This study will be conducted with the Multicenter Perioperative Outcomes Group, a national anesthesia quality improvement consortium with >60 member hospitals in >20 US states. This study will extend the Multicenter Perioperative Outcomes Group quality improvement infrastructure by using existing data and performance measurement processes.

RESULTS

The proposal was funded in September 2021 with a 4-year timeline. Data collection for Aim 1 began in March 2022. We plan for a 24-month trial timeline, with the intervention period of the trial beginning in March 2024.

CONCLUSIONS

The proposed aims will collectively demonstrate a precision feedback service developed using an open-source technical infrastructure for computable knowledge management. By implementing and evaluating a demonstration system for precision feedback, we create the potential to observe the conditions under which feedback interventions are effective.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/34990.

Generating and Reporting Electronic Clinical Quality Measures from Electronic Health Records: Strategies from EvidenceNOW Cooperatives

BACKGROUND

Electronic clinical quality measures (eCQMs) from electronic health records (EHRs) are a key component of quality improvement (QI) initiatives in small-to-medium size primary care practices, but using eCQMs for QI can be challenging. Organizational strategies are needed to effectively operationalize eCQMs for QI in these practice settings.

OBJECTIVE

This study aimed to characterize strategies that seven regional cooperatives participating in the EvidenceNOW initiative developed to generate and report EHR-based eCQMs for QI in small-to-medium size practices.

METHODS

A qualitative study comprised of 17 interviews with representatives from all seven EvidenceNOW cooperatives was conducted. Interviewees included administrators were with both strategic and cooperative-level operational responsibilities and external practice facilitators were with hands-on experience helping practices use EHRs and eCQMs. A subteam conducted 1-hour semistructured telephone interviews with administrators and practice facilitators, then analyzed interview transcripts using immersion crystallization. The analysis and a conceptual model were vetted and approved by the larger group of coauthors.

RESULTS

Cooperative strategies consisted of efforts in four key domains. First, cooperative adaptation shaped overall strategies for calculating eCQMs whether using EHRs, a centralized source, or a "hybrid strategy" of the two. Second, the eCQM generation described how EHR data were extracted, validated, and reported for calculating eCQMs. Third, practice facilitation characterized how facilitators with backgrounds in health information technology (IT) delivered services and solutions for data capture and quality and practice support. Fourth, performance reporting strategies and tools informed QI efforts and how cooperatives could alter their approaches to eCQMs.

CONCLUSION

Cooperatives ultimately generated and reported eCQMs using hybrid strategies because they determined neither EHRs alone nor centralized sources alone could operationalize eCQMs for QI. This required cooperatives to devise solutions and utilize resources that often are unavailable to typical small-to-medium-sized practices. The experiences from EvidenceNOW cooperatives provide insights into how organizations can plan for challenges and operationalize EHR-based eCQMs.

Design, Implementation, Utilization, and Sustainability of a Fast Healthcare Interoperability Resources-Based Inpatient Rounding List

OBJECTIVE

We designed and implemented an application programming interface (API)-based electronic health record (EHR)-integrated rounding list and evaluated acceptability, clinician satisfaction, information accuracy, and efficiency related to the application.

METHODS

We developed and integrated an application, employing iterative design techniques with user feedback. EHR and application user action logs, as well as hospital safety reports, were evaluated. Rounding preparation characteristics were obtained through surveys before and after application integration. To evaluate usability, inpatient providers, including residents, fellows, and attendings were surveyed 2 weeks prior to and 6 months after enterprise-wide EHR application integration. Our primary outcome was provider time savings measured by user action logs; secondary outcomes include provider satisfaction.

RESULTS

The application was widely adopted by inpatient providers, with more than 69% of all inpatients queried by the application within 6 months of deployment. Application utilization was sustained throughout the study period with 79% (interquartile range [IQR]: 76, 82) of enterprise-wide unique patients accessed per weekday. EHR action logs showed application users spent -3.24 minutes per day (95% confidence interval [CI]: -6.8, 0.33), p = 0.07 within the EHR compared with nonusers. Median self-reported chart review time for attendings decreased from 30 minutes (IQR: 15, 60) to 20 minutes (IQR: 10, 45) after application integration (p = 0.04). Self-reported sign-out preparation time decreased by a median of 5 minutes (p < 0.01), and providers were better prepared for hand-offs (p = 0.02). There were no increased safety reports during the study period.

CONCLUSION

This study demonstrates successful integration of a rounding application within a commercial EHR using APIs. We demonstrate increasing both provider-reported satisfaction and time savings. Rounding lists provided more accurate and timely information for rounds. Application usage was sustained across multiple specialties at 42 months. Other application designers should consider data density, optimization of provider workflows, and using real-time data transfer using novel tools when designing an application.

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.

Development of a repository of computable phenotype definitions using the clinical quality language

OBJECTIVE

The objective of this study is to create a repository of computable, technology-agnostic phenotype definitions for the purposes of analysis and automatic cohort identification.

MATERIALS AND METHODS

We selected phenotype definitions from PheKB and excluded definitions that did not use structured data or were not used in published research. We translated these definitions into the Clinical Quality Language (CQL) and Fast Healthcare Interoperability Resources (FHIR) and validated them using code review and automated tests.

RESULTS

A total of 33 phenotype definitions met our inclusion criteria. We developed 40 CQL libraries, 231 value sets, and 347 test cases. To support these test cases, a total of 1624 FHIR resources were created as test data.

DISCUSSION AND CONCLUSION

Although a number of challenges were encountered while translating the phenotypes into structured form, such as requiring specialized knowledge, or imprecise, ambiguous, and conflicting language, we have created a repository and a development environment that can be used for future research on computable phenotypes.

Harmonization of the ICHOM Quality Measures to Enable Health Outcomes Measurement in Multimorbid Patients

Objectives: To update the sets of patient-centric outcomes measures (“standard-sets”) developed by the not-for-profit organization ICHOM to become more readily applicable in patients with multimorbidity and to facilitate their implementation in health information systems. To that end we set out to (i) harmonize measures previously defined separately for different conditions, (ii) create clinical information models from the measures, and (iii) restructure the annotation to make the sets machine-readable.

Materials and Methods: First, we harmonized the semantic meaning of individual measures across all the 28 standard-sets published to date, in a harmonized measure repository. Second, measures corresponding to four conditions (Breast cancer, Cataracts, Inflammatory bowel disease and Heart failure) were expressed as logical models and mapped to reference terminologies in a pilot study.

Results: The harmonization of semantic meaning resulted in a consolidation of measures used across the standard-sets by 15%, from 3,178 to 2,712. These were all converted into a machine-readable format. 61% of the measures in the 4 pilot sets were bound to existing concepts in either SNOMED CT or LOINC.

Discussion: The harmonization of ICHOM measures across conditions is expected to increase the applicability of ICHOM standard-sets to multi-morbid patients, as well as facilitate their implementation in health information systems.

Conclusion: Harmonizing the ICHOM measures and making them machine-readable is expected to expedite the global adoption of systematic and interoperable outcomes measurement. In turn, we hope that the improved transparency on health outcomes that follows will let health systems across the globe learn from each other to the ultimate benefit of patients.

Informatics for public health and health system collaboration: Applications for the control of the current COVID-19 pandemic and the next one

Public health faces unprecedented challenges in its efforts to control COVID-19 through a national vaccination campaign. Addressing these challenges will require fundamental changes to public health data systems. For example, of the core data systems for immunization campaigns is the immunization information system (IIS); however, IISs were designed for tracking the vaccinated, not finding the patients who are high risk and need to be vaccinated. Health systems have this data in their electronic health records (EHR) systems and often have a greater capacity for outreach. Clearly, a partnership is needed. However, successful collaborations will require public health to change from its historical hierarchical information supply chain model to an ecosystem model with a peer-to-peer exchange with population health providers. Examples of the types of informatics innovations necessary to support such an ecosystem include a national patient identifier, population-level data exchange for immunization data, and computable electronic quality measures. Rather than think of these components individually, a comprehensive approach to rapidly adaptable tools for collaboration is needed.

Assessing Data Adequacy for High Blood Pressure Clinical Decision Support: A Quantitative Analysis

OBJECTIVE

This study examines guideline-based high blood pressure (HBP) and hypertension recommendations and evaluates the suitability and adequacy of the data and logic required for a Fast Healthcare Interoperable Resources (FHIR)-based, patient-facing clinical decision support (CDS) HBP application. HBP is a major predictor of adverse health events, including stroke, myocardial infarction, and kidney disease. Multiple guidelines recommend interventions to lower blood pressure, but implementation requires patient-centered approaches, including patient-facing CDS tools.

METHODS

We defined concept sets needed to measure adherence to 71 recommendations drawn from eight HBP guidelines. We measured data quality for these concepts for two cohorts (HBP screening and HBP diagnosed) from electronic health record (EHR) data, including four use cases (screening, nonpharmacologic interventions, pharmacologic interventions, and adverse events) for CDS.

RESULTS

We identified 102,443 people with diagnosed and 58,990 with undiagnosed HBP. We found that 21/35 (60%) of required concept sets were unused or inaccurate, with only 259 (25.3%) of 1,101 codes used. Use cases showed high inclusion (0.9-11.2%), low exclusion (0-0.1%), and missing patient-specific context (up to 65.6%), leading to data in 2/4 use cases being insufficient for accurate alerting.

DISCUSSION

Data quality from the EHR required to implement recommendations for HBP is highly inconsistent, reflecting a fragmented health care system and incomplete implementation of standard terminologies and workflows. Although imperfect, data were deemed adequate for two test use cases.

CONCLUSION

Current data quality allows for further development of patient-facing FHIR HBP tools, but extensive validation and testing is required to assure precision and avoid unintended consequences.