The Technology Landscape of Patient-Centered Clinical Decision Support – Where Are We and What Is Needed?

Dullabh, Prashila; Heaney-Huls, Krysta; Hovey, Lauren; Sandberg, Shana F.; Lobach, David F.; Boxwala, Aziz A.; Desai, Priyanka; Sittig, Dean F.

doi:10.3233/shti220094

Cited by 4 publications

(7 citation statements)

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“…According to PCORnet’s survey, the SDOH elements most commonly collected in structured EHR fields by their respondents included language collected by 64% of PCORnet survey respondents, food security (47%), education (47%), transportation (42%), employment status (42%), housing security (40%) and financial resource strain (38%) [ 46 ]. The PCORnet survey did not ask for completeness assessments at responding sites for these items.…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, two of our four institutions (UTH and UTSW) collected homeless or veteran status, which is twice the 1-in-4 rate reported among PCORnet survey respondents [ 46 ]. Because only two of our institutions collected homelessness or veteran status, we did not assess completeness of these measures.…”

Section: Discussionmentioning

confidence: 99%

“…The PCORnet survey highlighted that all participating institutions acknowledged the importance of SDOH data elements, and many health systems had plans to expand and enhance structured EHR collection of SDOH data. The PCORnet survey identified priority data elements to include food security, housing stability, financial resource strain, transportation needs, and education (mentioned by 51%, 49%, 42%, 40%, and 33% of their participants, respectively) [ 46 ]. In our study, housing instability, education, transportation, financial resource strain, and food security were collected by all sites.…”

Section: Discussionmentioning

confidence: 99%

“…In December 2021, we finalized a data collection instrument to inventory individual-level SDOH collected in structured EHR fields to be completed by each TRCC hub’s institutional Epic group. We incorporated 20 SDOH data elements that were reported as included in EHRs in results for the “2021 Survey on the Landscape of Collection and Use of SDOH” for institutions participating in the PCORnet clinical research network [ 46 ]. The PCORnet survey probed elements derived from recommendations by the PCORI SDOH workgroup, the National Academy of Medicine, CMS, and the Uniform Data System [ 46 ].…”

Section: Methodsmentioning

confidence: 99%

“…We incorporated 20 SDOH data elements that were reported as included in EHRs in results for the “2021 Survey on the Landscape of Collection and Use of SDOH” for institutions participating in the PCORnet clinical research network [ 46 ]. The PCORnet survey probed elements derived from recommendations by the PCORI SDOH workgroup, the National Academy of Medicine, CMS, and the Uniform Data System [ 46 ]. Based on additional elements that PCORnet survey participants prioritized for EHR-based collection, framework examination, and growing interest in more granular categorization of certain elements for health equity interventions, we added 10 additional elements to our data collection instrument.…”

Section: Methodsmentioning

confidence: 99%

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Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration

Craven,

Highfield,

Basit

et al. 2024

J. Clin. Trans. Sci.

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Introduction: The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods: Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub’s EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results: One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%–98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion: Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration

Craven,

Highfield,

Basit

et al. 2024

J. Clin. Trans. Sci.

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Avenues for Strengthening PCORnet’s Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR)

Waitman,

Bailey,

Becich

et al. 2023

Medical Care

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PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system “sites.” As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient’s health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI’s authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI’s expanded scope and PCORnet’s phase 3 beginning in January 2022, there are opportunities to strengthen the network’s ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.

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Fast Healthcare Interoperability Resources (FHIR)-Based Interoperability Design in Indonesia: Content Analysis of Developer Hub’s Social Networking Service (Preprint)

Heryawan,

Mori,

Yamamoto

et al. 2023

JMIR Formative Research

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Background: Interoperability in healthcare is a critical aspect for the exchange of health information. The Fast Healthcare Interoperability Resources (FHIR) framework has become widely adopted to provide interoperable data exchange in the healthcare industry. The COVID-19 pandemic has demonstrated the significance of interoperable data in tracking patients who have contracted the virus and keeping track of the vaccinated population. Indonesia is one of the many countries that have implemented interoperable data systems to track COVID-19 patients, and it has aspirations to expand the system to other use cases, particularly in the primary healthcare setting. The primary healthcare providers in Indonesia include Puskesmas (community health centers) and private clinics.Objective: To promote interoperable health data exchange in the primary healthcare sector, the Indonesian government has launched the Satusehat project. The goal of the Satusehat platform is to make health data in Indonesia interoperable and exchangeable between healthcare organizations, particularly Puskesmas and private clinics.Methods: For a successful implementation of the Satusehat platform in Puskesmas and private clinics, it is crucial to understand the challenges that may arise. This study analyzed the pain points of the Satusehat platform based on a content analysis of the Satusehat Social Networking Service (SNS) Telegram group messages. The study revealed the pain points and suggested existing approaches to address them, which can be used as a proposed design of interoperability for Puskesmas and private clinics, making it easier for these organizations to adopt the Satusehat platform. Results:The pain points identified in this study include issues with the FHIR server, problems with FHIR profile selection, and the mapping of EMR data into standardized data, such as mapping into the SNOMED-CT terminology. To address these issues, the study proposed practical approaches, including a federated architecture for the FHIR server instead of a centralized architecture, a FHIR writer and FHIR viewer system inspired by the Standardized Structured Medical Record Information eXchange (SS-MIX) system in Japan, and a FHIR conversion framework that integrates with our FHIR writer and FHIR viewer system.Conclusions: These proposed solutions can help resolve the pain points identified in the study and ensure the successful implementation of the Satusehat platform in Puskesmas and private clinics in Indonesia.

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The Technology Landscape of Patient-Centered Clinical Decision Support – Where Are We and What Is Needed?

Cited by 4 publications

References 0 publications

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration

Avenues for Strengthening PCORnet’s Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR)

Fast Healthcare Interoperability Resources (FHIR)-Based Interoperability Design in Indonesia: Content Analysis of Developer Hub’s Social Networking Service (Preprint)

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