Electronic Health Record Integration Research Articles

Development and Validation of an Automated, Real-Time Predictive Model for Postpartum Hemorrhage

OBJECTIVE: To develop and validate a predictive model for postpartum hemorrhage that can be deployed in clinical care using automated, real-time electronic health record (EHR) data and to compare performance of the model with a nationally published risk prediction tool. METHODS: A multivariable logistic regression model was developed from retrospective EHR data from 21,108 patients delivering at a quaternary medical center between January 1, 2018, and April 30, 2022. Deliveries were divided into derivation and validation sets based on an 80/20 split by date of delivery. Postpartum hemorrhage was defined as blood loss of 1,000 mL or more in addition to postpartum transfusion of 1 or more units of packed red blood cells. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) and was compared with a postpartum hemorrhage risk assessment tool published by the CMQCC (California Maternal Quality Care Collaborative). The model was then programmed into the EHR and again validated with prospectively collected data from 928 patients between November 7, 2023, and January 31, 2024. RESULTS: Postpartum hemorrhage occurred in 235 of 16,862 patients (1.4%) in the derivation cohort. The predictive model included 21 risk factors and demonstrated an AUC of 0.81 (95% CI, 0.79–0.84) and calibration slope of 1.0 (Brier score 0.013). During external temporal validation, the model maintained discrimination (AUC 0.80, 95% CI, 0.72–0.84) and calibration (calibration slope 0.95, Brier score 0.014). This was superior to the CMQCC tool (AUC 0.69 [95% CI, 0.67–0.70], P<.001). The model maintained performance in prospective, automated data collected with the predictive model in real time (AUC 0.82 [95% CI, 0.73–0.91]). CONCLUSION: We created and temporally validated a postpartum hemorrhage prediction model, demonstrated its superior performance over a commonly used risk prediction tool, successfully coded the model into the EHR, and prospectively validated the model using risk factor data collected in real time. Future work should evaluate external generalizability and effects on patient outcomes; to facilitate this work, we have included the model coefficients and examples of EHR integration in the article.

Healthcare systems collaborating to implement a shared decision‐making tool in the electronic health record and build evidence on its adoption and use

AbstractIntroductionShared decision‐making (SDM) is a method of care by which patients and clinicians work together to co‐create a plan of care. Electronic health record (EHR) integration of SDM tools may increase adoption of SDM. We conducted a “lightweight” integration of a freely available electronic SDM tool, CV Prevention Choice, within the EHRs of three healthcare systems. Here, we report how the healthcare systems collaborated to achieve integration.MethodsThis work was conducted as part of a stepped wedge randomized pragmatic trial. CV Prevention Choice was developed using guidelines for HTML5‐based web applications. Healthcare systems integrated the tool in their EHR using documentation the study team developed and refined with lessons learned after each system integrated the electronic SDM tool into their EHR. CV Prevention Choice integration populates the tool with individual patient data locally without sending protected health information between the EHR and the web. Data abstraction and secure transfer systems were developed to manage data collection to assess tool implementation and effectiveness outcomes.ResultsTime to integrate CV Prevention Choice in the EHR was 12.1 weeks for the first system, 10.4 weeks for the second, and 9.7 weeks for the third. One system required two 1‐hour meetings with study team members and two healthcare systems required a single 1‐hour meeting. Healthcare system information technology teams collaborated by sharing information and offering improvements to documentation. Challenges included tracking CV Prevention Choice use for reporting and capture of combination medications. Data abstraction required refinements to address differences in how each healthcare system captured data elements.ConclusionTargeted documentation on tool features and resource mapping supported collaboration of IT teams across healthcare systems, enabling them to integrate a web‐based SDM tool with little additional research team effort or oversight. Their collaboration helped overcome difficulties integrating the web application and address challenges to data harmonization for trial outcome analyses.

Opioid Use Disorder Treatment Prescribers’ Views of Implementing an Electronic System to Collect Patient-Reported Outcomes Measures

Use of patient-reported outcomes measures (PROMs) may improve addiction treatment outcomes. This qualitative study investigated barriers and facilitators for PROMs implementation among prescribers of medications for opioid use disorder (MOUD) participating in AMNet, an addiction medicine practice-based research network. Semi-structured interviews were conducted with seven qualitative research participants of 57 AMNet providers. Interview data were analyzed thematically and coded using ATLAS.ti. Barriers included lack of electronic health record (EHR) integration; user interface issues; insufficient administrative support; challenges with patient engagement; and lack of fit with clinical practice. Facilitators included perceptions that PROMs use would improve outcomes and increase accountability while advancing addiction medicine. Patient-reported outcomes measures implementation is feasible in small outpatient MOUD practices, although resource constraints and technological barriers may reduce potential reach.

Changes in opioid prescribing and prescription drug monitoring program utilization following electronic health record integration-Massachusetts, 2018.

In this study we explored key prescription drug monitoring program-related outcomes among clinicians from a broad cohort of Massachusetts healthcare facilities following prescription drug monitoring program (PDMP) and electronic health record (EHR) data integration. Outcomes included 7-day rolling averages of opioids prescribed, morphine milligram equivalents (MMEs) prescribed, and PDMP queries. We employed a longitudinal study design to analyze PDMP data over a 15-month study period which allowed for six and a half months of pre- and post-integration observations surrounding a two-month integration period. We used longitudinal mixed effects models to examine the effect of EHR integration on each of the key outcomes. Following EHR integration, PDMP queries increased both through the web-based portal and in total (0.037, [95% CI = 0.017, 0.057] and 0.056, [95% CI = 0.035, 0.077]). Both measures of clinician opioid prescribing declined throughout the study period; however no significant effect following EHR integration was observed. These results were consistent when our analysis was applied to a subset consisting only of continuous PDMP users. Our results support EHR integration contributing to PDMP utilization by clinicians but do not support changes in opioid prescribing behavior.

Ambient Artificial Intelligence Scribes to Alleviate the Burden of Clinical Documentation

Clinical documentation in the electronic health record (EHR) has become increasingly burdensome for physicians and is a major driver of clinician burnout and dissatisfaction. Time dedicated to clerical activities and data entry during patient encounters also negatively affects the patient–physician relationship by hampering effective and empathetic communication and care. Ambient artificial intelligence (AI) scribes, which use machine learning applied to conversations to facilitate scribe-like capabilities in real time, has great potential to reduce documentation burden, enhance physician–patient encounters, and augment clinicians’ capabilities. The technology leverages a smartphone microphone to transcribe encounters as they occur but does not retain audio recordings. To address the urgent and growing burden of data entry, in October 2023, The Permanente Medical Group (TPMG) enabled ambient AI technology for 10,000 physicians and staff to augment their clinical capabilities across diverse settings and specialties. The implementation process leveraged TPMG’s extensive experience in large-scale technology instantiation and integration incorporating multiple training formats, at-the-elbow peer support, patient-facing materials, rapid-cycle upgrades with the technology vendor, and ongoing monitoring. In 10 weeks since implementation, the ambient AI tool has been used by 3,442 TPMG physicians to assist in as many as 303,266 patient encounters across a wide array of medical specialties and locations. In total, 968 physicians have enabled ambient AI scribes in ≥100 patient encounters, with one physician having enabled it to assist in 1,210 encounters. The response from physicians who have used the ambient AI scribe service has been favorable; they cite the technology’s capability to facilitate more personal, meaningful, and effective patient interactions and to reduce the burden of after-hours clerical work. In addition, early assessments of patient feedback have been positive, with some describing improved interaction with their physicians. Early evaluation metrics, based on an existing tool that evaluates the quality of human-generated scribe notes, find that ambient AI use produces high-quality clinical documentation for physicians’ editing. Further statistical analyses after AI scribe implementation also find that usage is linked with reduced time spent in documentation and in the EHR. Ongoing enhancements of the technology are needed and are focused on direct EHR integration, improved capabilities for incorporating medical interpretation, and enhanced workflow personalization options for individual users. Despite this technology’s early promise, careful and ongoing attention must be paid to ensure that the technology supports clinicians while also optimizing ambient AI scribe output for accuracy, relevance, and alignment in the physician–patient relationship.

Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial.

We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (LINK-HF2). A mixed-method analysis was conducted at 2 pilot sites. Interviews were conducted with 12 of 27 enrolled patients and with 13 participating clinicians. iPARIHS constructs were used for interview construction to identify workflow, communication patterns, and clinician's beliefs. Interviews were transcribed and analyzed using inductive coding protocols to identify key themes. Behavioral response data from the AI-generated notifications were collected. Clinicians responded to notifications within 24 hours in 95% of instances, with 26.7% resulting in clinical action. Four implementation themes emerged: (1) High anticipatory expectations for reliable patient communications, reduced patient burden, and less proactive provider monitoring. (2) The AI notifications required a differential and tailored balance of trust and action advice related to role. (3) Clinic experience with other home-based programs influenced utilization. (4) Responding to notifications involved significant effort, including electronic health record (EHR) review, patient contact, and consultation with other clinicians. Clinician's use of AI data is a function of beliefs regarding the trustworthiness and usefulness of the data, the degree of autonomy in professional roles, and the cognitive effort involved. The implementation planning analysis guided development of strategies that addressed communication technology, patient education, and EHR integration to reduce clinician and patient burden in the subsequent main randomized phase of the trial. Our results provide important insights into the unique implications of implementing AI analytics into clinical workflow.

Examining rehabilitation access disparities: an integrated analysis of electronic health record data and population characteristics through bivariate choropleth mapping.

Despite efforts to view electronic health records (EHR) data through an equity lens, crucial contextual information regarding patients' social environments remains limited. Integrating EHR data and Geographic Information Systems (GIS) technology can give deeper insights into the relationships between patients' social environments, health outcomes, and geographic factors. This study aims to identify regions with the fastest and slowest access to outpatient physical therapy services using bivariate choropleth maps to provide contextual insights that may contribute to health disparity in access. This was a retrospective cohort study of patients' access timelines for the first visit to outpatient physical therapy services (n = 10,363). The three timelines evaluated were (1) referral-to-scheduled appointment time, (2) scheduled appointment to first visit time, and (3) referral to first visit time. Hot and coldspot analyses (CI 95%) determined the fastest and slowest access times with patient-level characteristics and bivariate choropleth maps that were developed to visualize associations between access patterns and disadvantaged areas using Area Deprivation Index scores. Data were collected between January 1, 2016 and January 1, 2020. EHR data were geocoded via GIS technology to calculate geospatial statistics (Gi∗ statistic from ArcGIS Pro) in an urban area. Statistically significant differences were found for all three access timelines between coldspot (i.e., fast access group) and hotspot (i.e., slow access group) comparisons (p < .05). The hotspot regions had higher deprivation scores; higher proportions of residents who were older, privately insured, female, lived further from clinics; and a higher proportion of Black patients with orthopaedic diagnoses compared to the coldspot regions. Our study identified and described local areas with higher densities of patients that experienced longer access times to outpatient physical therapy services. Integration of EHR and GIS data is a more robust method to identify health disparities in access to care. With this approach, we can better understand the intricate interplay between social, economic, and environmental factors contributing to health disparities in access to care.

Design and Implementation of an Electronic Health Record-Integrated Hypertension Management Application.

High blood pressure affects approximately 116 million adults in the United States. It is the leading risk factor for death and disability across the world. Unfortunately, over the past decade, hypertension control rates have decreased across the United States. Prediction models and clinical studies have shown that reducing clinician inertia alone is sufficient to reach the target of ≥80% blood pressure control. Digital health tools containing evidence-based algorithms that are able to reduce clinician inertia are a good fit for turning the tide in blood pressure control, but careful consideration should be taken in the design process to integrate digital health interventions into the clinical workflow. We describe the development of a provider-facing hypertension management platform. We enumerate key steps of the development process, including needs finding, clinical workflow analysis, treatment algorithm creation, platform design and electronic health record integration. We interviewed and surveyed 5 Stanford clinicians from primary care, cardiology, and their clinical care team members (including nurses, advanced practice providers, medical assistants) to identify needs and break down the steps of clinician workflow analysis. The application design and development stage were aided by a team of approximately 15 specialists in the fields of primary care, hypertension, bioinformatics, and software development. Digital monitoring holds immense potential for revolutionizing chronic disease management. Our team developed a hypertension management platform at an academic medical center to address some of the top barriers to adoption and achieving clinical outcomes. The frameworks and processes described in this article may be used for the development of a diverse range of digital health tools in the cardiovascular space.

Next-generation study databases require FAIR, EHR-integrated, and scalable Electronic Data Capture for medical documentation and decision support

Structured patient data play a key role in all types of clinical research. They are often collected in study databases for research purposes. In order to describe characteristics of a next-generation study database and assess the feasibility of its implementation a proof-of-concept study in a German university hospital was performed. Key characteristics identified include FAIR access to electronic case report forms (eCRF), regulatory compliant Electronic Data Capture (EDC), an EDC with electronic health record (EHR) integration, scalable EDC for medical documentation, patient generated data, and clinical decision support. In a local case study, we then successfully implemented a next-generation study database for 19 EDC systems (n = 2217 patients) that linked to i.s.h.med (Oracle Cerner) with the local EDC system called OpenEDC. Desiderata of next-generation study databases for patient data were identified from ongoing local clinical study projects in 11 clinical departments at Heidelberg University Hospital, Germany, a major tertiary referral hospital. We compiled and analyzed feature and functionality requests submitted to the OpenEDC team between May 2021 and July 2023. Next-generation study databases are technically and clinically feasible. Further research is needed to evaluate if our approach is feasible in a multi-center setting as well.

Enhancing electronic health record integration in large hospital environments: Addressing challenges, training, and cultivating organizational awareness

Enhancing electronic health record integration in large hospital environments: Addressing challenges, training, and cultivating organizational awareness

Integrated Electronic Health Record of Multidisciplinary Professionals Throughout the Cancer Care Pathway: A Pilot Study Exploring Patient-Centered Information in Breast Cancer Patients.

The aim of this pilot study was to first aggregate and then integrate the medical records of various healthcare professionals involved with breast cancer patients to reveal if and how patient-centered information is documented in multidisciplinary cancer care. We aggregated 20 types of medical records from various healthcare professionals such as physicians, nurses and allied healthcare professionals (AHPs) throughout three breast cancer patients' care pathways in a department of breast surgery at a university hospital. Purposeful sampling was used, and three cases were examined. The number of integrated type of records was 14, 14, 17 in case 1, 2 and 3, respectively. We manually annotated and analyzed them exploratively using a thematic analysis. The tags were produced using both a deductive template approach and a data-driven inductive approach. All records were then given tags. We defined patient-centered information related tags and biomedical information related tags and then analyzed for if and how patient-centered information was documented. The number of patient-centered information related tags accounted for 30%, 30% and 20% of the total in case 1, 2 and 3, respectively. In all cases, patient-centered information was distributed across various medical records. The Progress Note written by doctors provided much of the patient-centered information, while other records contained information not described elsewhere in the Progress Notes. The records of nurses and AHPs included more patient-centered information than the doctors' notes. Each piece of patient-centered information was documented in fragments providing from each of the healthcare professionals' viewpoints. The documented information throughout the breast cancer care pathway in the cases examined was dominated by biomedical information. However, our findings suggest that integrating fragmented patient-centered information from various healthcare professionals' medical records produces holistic patient-centered information from multiple perspectives and thus may facilitate an enhanced multidisciplinary patient-centered care.

Deep learning prediction of hospital readmissions for asthma and COPD

QuestionSevere asthma and COPD exacerbations requiring hospitalization are linked to increased disease morbidity and healthcare costs. We sought to identify Electronic Health Record (EHR) features of severe asthma and COPD exacerbations and evaluate the performance of four machine learning (ML) and one deep learning (DL) model in predicting readmissions using EHR data.Study design and methodsObservational study between September 30, 2012, and December 31, 2017, of patients hospitalized with asthma and COPD exacerbations.ResultsThis study included 5,794 patients, 1,893 with asthma and 3,901 with COPD. Patients with asthma were predominantly female (n = 1288 [68%]), 35% were Black (n = 669), and 25% (n = 479) were Hispanic. Black (44 vs. 33%, p = 0.01) and Hispanic patients (30 vs. 24%, p = 0.02) were more likely to be readmitted for asthma. Similarly, patients with COPD readmissions included a large percentage of Blacks (18 vs. 10%, p < 0.01) and Hispanics (8 vs. 5%, p < 0.01). To identify patients at high risk of readmission index hospitalization data of a subset of 2,682 patients, 777 with asthma and 1,905 with COPD, was analyzed with four ML models, and one DL model. We found that multilayer perceptron, the DL method, had the best sensitivity and specificity compared to the four ML methods implemented in the same dataset.InterpretationMultilayer perceptron, a deep learning method, had the best performance in predicting asthma and COPD readmissions, demonstrating that EHR and deep learning integration can improve high-risk patient detection.

Design of a Bilingual and Bicultural Dementia Screening Toolkit Integrated in the Electronic Health Record

AbstractBackgroundBased predominantly on longitudinal cohort studies, the prevalence of Alzheimer’ disease (AD) is estimated to be 3‐10% in individuals over 65 years old and has increased risk (1.5‐2.0 times) in Black and Hispanic/Latinx individuals. In our prior analyses of the University of California Los Angeles (UCLA) electronic health record (EHR), we found that only 1% of patients over 65 years old are diagnosed with AD, and AD is less likely to be diagnosed in Black and Hispanic/Latinx patients compared to white patients (relative risk 0.86‐0.9). One solution to the underdiagnosis of AD in real‐world clinical care, is to screen for dementia.MethodsRecent work by several University California AD Centers and the California Department of Public Health developed a short (&lt;5 min) dementia screening tool (DST). The DST includes a three‐question questionnaire for the patient and an informant if present, followed by the Mini‐Cog if applicable (Figure 1). The questions ask about changes in language, memory, and personality (Figure 2). The DST is considered positive if any question is answered “Yes” by the patient or informant, or if the Mini‐Cog score is &lt;3 out of 5 points. To improve care for non‐English speaking patients, we developed the questionnaire in Spanish, which was adapted to Hispanic/Latinx culture.ResultsThe DST is integrated in the EHR. The questionnaires are sent to patients prior to the visit via the messaging system. Answers to the questionnaire and the Mini‐Cog are entered into a flowsheet in an easily accessible EHR tab. We also created a documentation link that automatically loads the DST results, final score, and recommendation into provider notes. The DST is live at a diverse UCLA family medicine clinic, which sees HL and Black patients. In the first three weeks, 178 patients were screened with the toolkit and eight patients were referred for specialty care.ConclusionsWe have developed a cultural and linguistic responsive DST that is integrated in the EHR. We will continue evaluating its ability to increase appropriate diagnosis of dementia in diverse racial and ethnic groups, and share our DST including EHR integration so it can be implemented elsewhere.

Abstract 325: Two-Year Outcomes of an Electronic Health Record Integrated Rapid Response Team Model

Background: Literature supports improved patient outcomes with dedicated resources for rapid response to patient deterioration in inpatient settings. However, studies of the association of centralized resource allocation and electronic health record (EHR) integration on patient outcomes are lacking. Aim: To improve early, centralized rapid response to patient deterioration and reduce unanticipated transfers to the ICU. Methods: Using a prospective longitudinal quality improvement design, we implemented a centralized, EHR supported patient response team (PRT) at an academic medical center in the southeastern United States. The model served 1,043 inpatient beds. Pre-intervention the PRT used episodic data capture and included 1 critical care RN, 1 physician, and 1 respiratory therapist; each expected to leave assigned patients to respond to non-ICU events. We implemented a dedicated PRT (4 critical care RNs and 1 hospitalist) to provide 24/7 response to code blue, RRT, and stroke code events. The team provided proactive rounding, preventive consultation, and assessment of patients from ICU or emergency within 24 hours. Standardized workflow and outcome documentation was integrated in the EHR. Improvement in processes and outcomes were analyzed using statistical process control charts with 25 data points over a 2-year period. Results: At baseline (FY 2020), the PRT responded to 1739 rapid response calls and 166 code blue events. Unanticipated transfer to ICU occurred in 935 (53.8%) patients. Post-implementation of the integrated PRT model, the rate of early rapid response calls improved (FY 2021 n=2099; FY 2022 n=2239), code blue events decreased (FY 2021 n=151; FY 2022 n=115), and unanticipated ICU transfers also decreased (25%; Figure 1). Conclusions: A centralized PRT model using technology-supported EHR integration, pre-defined workflows, and a dedicated team can reduce non-ICU cardiac arrests and decrease unanticipated transfers to ICU.

E-PainSupport: A Digital Pain Management Application for Home Hospice Care.

e-PainSupport is a digital pain management application (app) designed to facilitate better pain management in hospice. Early testing revealed caregivers found it was easy to use and successful in communicating patient pain and caregiver administration of analgesics to hospice nurses. However, caregiver knowledge of analgesic management remained low. The purpose of this study was to enhance e-PainSupport by (a) adapting and integrating an evidence-based pain educational intervention; (b) increasing ease of accessing and navigating the app; and (c) facilitating app communication with agency electronic health records (EHRs). An advisory board method, including two key stakeholder groups (an expert panel and a caregiver advisory board), guided the adaptation of an evidence-based pain educational intervention. Further, stakeholders recommended format changes to increase app usability. Study staff worked with four hospice agencies to facilitate app communication with EHRs. While modification to the e-PainSupport app to integrate a pain educational intervention and facilitate usability was successful, EHR integration was challenging. Future evaluation is needed to evaluate the effects of e-PainSupport on pain intensity among home hospice patients.

Role of Individual Clinician Authority in the Implementation of Informatics Tools for Population-Based Medication Management: Qualitative Semistructured Interview Study.

Direct oral anticoagulant (DOAC) medications are frequently associated with inappropriate prescribing and adverse events. To improve the safe use of DOACs, health systems are implementing population health tools within their electronic health record (EHR). While EHR informatics tools can help increase awareness of inappropriate prescribing of medications, a lack of empowerment (or insufficient empowerment) of nonphysicians to implement change is a key barrier. This study examined how the individual authority of clinical pharmacists and anticoagulation nurses is impacted by and changes the implementation success of an EHR DOAC Dashboard for safe DOAC medication prescribing. We conducted semistructured interviews with pharmacists and nurses following the implementation of the EHR DOAC Dashboard at 3 clinical sites. Interview transcripts were coded according to the key determinants of implementation success. The intersections between individual clinician authority and other determinants were examined to identify themes. A high level of individual clinician authority was associated with high levels of key facilitators for effective use of the DOAC Dashboard (communication, staffing and work schedule, job satisfaction, and EHR integration). Conversely, a lack of individual authority was often associated with key barriers to effective DOAC Dashboard use. Positive individual authority was sometimes present with a negative example of another determinant, but no evidence was found of individual authority co-occurring with a positive instance of another determinant. Increased individual clinician authority is a necessary antecedent to the effective implementation of an EHR DOAC Population Management Dashboard and positively affects other aspects of implementation. RR2-10.1186/s13012-020-01044-5.

Preserving Decentralized EHR-s Integrity.

A blockchain and decentralized storage technology-based architecture is proposed to support the integrity of Electronic Health Records (EHRs).

Using Electronic Health Record System to Establish a National Patient's Registry : Lessons learned from the Cancer Registry in Iran

BackgroundIn Iran, the Integrated Electronic Health Record system, called SEPAS, has been established to store all patient encounters of individuals referring to healthcare facilities. ObjectiveWe aimed to develop a model for cleaning SEPAS and applying its data in other databases. MethodsWe used cancer data from SEPAS as the sample. We developed a guideline to identify codes for cancer-related diagnoses and services in the database. Furthermore, we searched the SEPAS database based on ICD-10 and the diagnosis description in English and Farsi in an Excel sheet. We added codes and descriptions of pharmaceuticals and procedures to the list. We applied the above database and linked it to the patient records to identify cancer patients. A dashboard was designed based on this information for every cancer patient. ResultsWe selected 5,841 diagnostic codes and phrases, 9,300 cancer pharmaceutics codes, and 452 codes from cancer-specific items related to the diagnostic procedures and treatment methods. Linkage of this list to the patient list generated a database of about 197,164 cancer patients for linkage in the registry database. ConclusionsPatient registries are one of the most important sources of information in healthcare systems. Data linkage between Electronic Health Record Systems (EHRs) and registries, despite its challenges, is profitable. EHRs can be used for case finding in any patient registry to reduce the time and cost of case finding.

From “Local Control” to “Dependency”: Transitions to Single-Vendor Integrated Electronic Health Record Systems and Their Implications for the EHR Workforce

BackgroundHealthcare systems that previously used either a single legacy electronic health record (EHR) system or a “best-of-breed” combination of products from multiple vendors are increasingly adopting integrated, single-vendor EHR systems. Though healthcare leaders are beginning to recognize the dramatic collateral consequences of these transitions, their impact on the EHR workforce — internal actors most closely involved in governing and supporting the EHR — is poorly understood.ObjectiveIdentify perceived impacts of adopting single-vendor, integrated EHR systems on the institutional EHR workforce.DesignIn this qualitative study, we conducted semi-structured phone interviews in four healthcare systems in the USA that had adopted an integrated EHR within the previous five years.ParticipantsForty-two staff members of four geographically and organizationally diverse healthcare systems, including 22 individuals with formal informatics roles.ApproachTranscribed interviews were coded and analyzed using qualitative content analysis methods.Key ResultsAcross organizations, participants described a loss of autonomy by the EHR workforce at the individual and institutional level following the adoption of an integrated EHR. We also identified references to transformations in four key professional functions of the EHR workforce: communication, governance, optimization, and education.ConclusionsTransitions to integrated EHR systems can have important implications for the autonomy and professional functions of the EHR workforce. These findings may help institutions embarking on similar transitions better anticipate and prepare for these changes through such practices as revising job descriptions, strengthening EHR governance structures, and reinforcing pathways to engage frontline clinicians in supporting the EHR. Findings may also help institutions structure vendor contracts in a way that anticipates and mitigates loss of autonomy.

ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines.

Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans. We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists. Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise. An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further.