Health Information Exchange Data Research Articles

Preparing community pharmacy teams for health information exchange (HIE)

BackgroundHealth information exchanges (HIEs) facilitate health care professionals’ electronic sharing of patient information across different organizations. When community pharmacists have access to HIE, they can further contribute to improved patient outcomes. However, several implementation challenges are noted, which impede sustained pharmacist access to HIE. To our knowledge, no bidirectional HIE interface design and pharmacy team-informed implementation process has been documented. In response, our research team designed and developed an HIE interface prototype for use specifically by community pharmacy teams to access local HIE data through their pharmacy dispensing software. ObjectivesTo 1) identify barriers, facilitators, and recommendations for using HIE data in community pharmacies and 2) create a curated list of resources addressing identified implementation needs to aid future implementation of a fully functional, bidirectional HIE interface by community pharmacy teams. MethodsPharmacists, pharmacy technicians, and patients from three pharmacy sites within the Community Pharmacy Enhanced Services Network of Indiana participated in individual semi-structured interviews. Interview questions were mapped to select constructs across all domains of the Consolidated Framework for Implementation Research. Interview transcripts were deductively coded. A subset of participants participated in Evidence-Based Quality Improvement sessions to iteratively update planned resource items needed to support future HIE implementation. ResultsWe interviewed 23 total participants: 8 pharmacists, 8 pharmacy technicians, and 7 patients. Five facilitators, four barriers, and two recommendations were identified. These were further characterized into four key implementation needs: instruction on how to use HIE; guidance on workflow and team roles; resources that are patient-facing; and resources that are provider-facing, resulting in 16 planned implementation resources. ConclusionOur study provides the first-of-its-kind list of pharmacy team-informed resources to facilitate sustainability and scalability of HIE implementation in community pharmacies.

Using a Health Information Exchange to Characterize Changes in HIV Viral Load Suppression and Disparities During the COVID-19 Pandemic in New York City.

HIV viral suppression requires sustained engagement in care. The COVID-19 pandemic challenged care accessibility for many people living with HIV (PLWH). We used health information exchange data to evaluate the effect of pandemic-related disruptions in HIV care on viral load suppression (VLS) and to examine racial/ethnic disparities in VLS. We performed a retrospective observational cohort study of PLWH using data from a regional health information exchange in the New York City region between 1 January 2018 and 31 December 2022. We established 2 cohorts: PLWH who received HIV care in 2020 (cohort A) and PLWH who did not receive HIV care in 2020 (cohort B). We categorized HIV VLS outcomes as suppressed or not suppressed and calculated the prevalence of VLS between 2018 and 2022. We compared proportions using chi-square tests and used unadjusted and adjusted logistic regression to estimate the association among variables, including race/ethnicity, cohort, and VLS. Of 5 301 578 patients, 34 611 met our inclusion criteria for PLWH, 11 653 for cohort A, and 3141 for cohort B. In 2019, cohort B had a lower prevalence of VLS than cohort A (86% vs 89%, P < .001). Between 2019 and 2021, VLS dropped significantly among cohort B (86% to 81%, P < .001) while staying constant in cohort A (89% to 89%, P = .62). By 2022, members of cohort B were less likely than cohort A to be receiving HIV care in New York City (74% vs 88%, P < .001). Within both cohorts, Black and Hispanic patients had lower odds of VLS than White patients. In New York City, VLS remained high among PLWH who continued to receive care in 2020 and dropped among PLWH who did not receive care. VLS was lower among Black and Hispanic patients even after controlling for receipt of care.

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications.

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare's path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

Usability Evaluation of a Community Pharmacy Health Information Exchange Interface Prototype.

Few community pharmacies have access to health information exchange (HIE) data. We conducted a first-of-its-kind usability evaluation of an HIE interface prototype (referred to throughout as the "HIE-Pioneer mock-up") developed with pharmacists and pharmacy technicians to aid future implementation in community pharmacies. Community pharmacists and pharmacy technicians were recruited to complete usability evaluations with the HIE-Pioneer mock-up. Each usability evaluation lasted up to 60 minutes. System usability scale (SUS) scores were collected from each participant following each usability evaluation session and summarized with descriptive statistics. Usability evaluation videos were reviewed for common usability attributes, such as the impact of identified usability problems, learnability, and efficiency. Time on task, task success rates, and prototype utilization were also recorded. Sixteen total participants completed usability testing across three community pharmacies. The average SUS score was 69.7 (scale 0-100, where 100 is the best), with pharmacists on average reporting higher satisfaction than technicians (74.1 vs. 65.3, respectively). Altogether, we identified 23 distinct usability problems. Key problems identified included needed clarification in tool label names and accessibility of HIE links within the existing workflow. Overall, the usability of the HIE-Pioneer mock-up generally fostered pharmacy professionals' ease of learning and efficiency. Our study identified key areas, and potential solutions, to improve the usability of the HIE-Pioneer mock-up. Overall, pharmacy professionals viewed the HIE-Pioneer mock-up positively, with good satisfaction ratings. The HIE-Pioneer mock-up provides a blueprint for future HIE implementation in community pharmacy settings, which would increase community pharmacy teams' access to HIE data nationwide. Community pharmacy access to bi-directional HIE is expected to improve communication among more health care professionals involved in patient care and equip pharmacy professionals with needed information for improved clinical decision-making.

Comparison of health information exchange data with self-report in measuring cancer screening

BackgroundEfficient measurement of the receipt of cancer screening has been attempted with electronic health records (EHRs), but EHRs are commonly implemented within a single health care setting. However, health information exchange (HIE) includes EHR data from multiple health care systems and settings, thereby providing a more population-based measurement approach. In this study, we set out to understand the value of statewide HIE data in comparison to survey self-report (SR) to measure population-based cancer screening.MethodsA statewide survey was conducted among residents in Indiana who had been seen at an ambulatory or inpatient clinical setting in the past year. Measured cancer screening tests included colonoscopy and fecal immunochemical test (FIT) for colorectal cancer, human papilloma virus (HPV) and Pap tests for cervical cancer, and mammogram for breast cancer. For each screening test, the self-reported response for receipt of the screening (yes/no) and ‘time since last screening’ were compared with the corresponding information from patient HIE to evaluate the concordance between the two measures.ResultsGwet’s AC for HIE and self-report of screening receipt ranged from 0.24–0.73, indicating a fair to substantial concordance. For the time since receipt of last screening test, the Gwet’s AC ranged from 0.21–0.90, indicating fair to almost perfect concordance. In comparison with SR data, HIE data provided relatively more additional information about laboratory-based tests: FIT (19% HIE alone vs. 4% SR alone) and HPV tests (27% HIE alone vs. 12% SR alone) and less additional information about procedures: colonoscopy (8% HIE alone vs. 23% SR alone), Pap test (13% HIE alone vs. 19% SR alone), or mammography (9% HIE alone vs. 10% SR alone).ConclusionStudies that use a single data source should consider the type of cancer screening test to choose the optimal data collection method. HIE and self-report both provided unique information in measuring cancer screening, and the most robust measurement approach involves collecting screening information from both HIE and patient self-report.

Characterizing suicidal ideation, suicidal behaviors, and service utilization among unhoused individuals using a health information exchange.

Unhoused individuals have high rates of suicidal ideation (SI)and suicidal behaviors (SB),but few have studied the relative timing of homelessness and SI/SB.Our study examines the potential to use state-wide electronic health record data from Rhode Island's health information exchange (HIE)to identify temporal relationships, service utilization, and associations of SI/SBamong unhoused individuals. We use timestamped HIE data for 5368 unhoused patients to analyze service utilization and the relative timing of homelessness versus SI/SBonset. Multivariable models identified associations of SI/SB,hospitalization, and repeat acute care utilization within 30 days from clinical features representing 10,000+ diagnoses captured within the HIE. The onset of SI typically precedes homelessness onset, while the onset of SB typically follows. Weekly rates of suicide-related service utilization increased over 25 times the baseline rate during the week before and after homelessness onset. Over 50% of encounters involving SI/SB result in hospitalization. Of those engaging in acute care for suicide-related reasons, we found high rates of repeat acute care encounters. HIEs are a particularly valuable resource for understudied populations. Our study demonstrates how longitudinal, multi-institutional data from an HIE can be used to characterize temporal associations, service utilization, and clinical associations of SI and behaviors among a vulnerable population at scale. Increasing access to services that address co-occurring SI/SB, mental health, and substance use is needed.

127 A formative usability evaluation of a community pharmacist-facing health information exchange (HIE) interface

OBJECTIVES/GOALS: To evaluate the usability of a HIE interface design among community pharmacists and technicians to identify opportunities for design improvements METHODS/STUDY POPULATION: Pharmacists and pharmacy technicians employed at Indiana community pharmacies participated in formative usability testing, via a Rapid Usability Evaluation (RUE), with an interactive, PDF prototype of the HIE interface. Participants were video-recorded to capture first impressions on the usability of the HIE prototype via the think aloud technique. Each participant had up to 1 hour to complete 4 clinical scenarios. Afterwards, participants completed the System Usability Scale (SUS; scale 0–100, with 100 being the best) to rate their satisfaction with the HIE prototype. RESULTS/ANTICIPATED RESULTS: Across 3 community pharmacies, 16 individuals participated in usability testing: 8 pharmacists and 8 technicians. The average SUS score for the HIE interface across participants from all sites was 70. Pharmacists on average scored the interface higher than technicians, 74 vs. 65, respectively. Initial findings from one pharmacy revealed that both pharmacists and technicians expressed a desire for improved efficiency (i.e. fewer clicks ) to access HIE data, alternative placement of HIE links within existing systems, and improved navigation to exit HIE links. DISCUSSION/SIGNIFICANCE: Initial results reveal opportunities to improve the HIE interface usability. Findings will inform design improvements to the interface and the creation of a toolkit to support the sustainable and scalable participation in HIE by community pharmacies.

Recent tapering from long-term opioid therapy and odds of opioid-related hospital use.

The number of patients tapered from long-term opioid therapy (LTOT) has increased in recent years in the United States. Some patients tapered from LTOT report improved quality of life, while others face increased risks of opioid-related hospital use. Research has not yet established how the risk of opioid-related hospital use changes across LTOT dose and subsequent tapering. Our objective was to examine associations between recent tapering from LTOT with odds of opioid-related hospital use. Case-crossover design using 2014-2018 health information exchange data from Indiana. We defined opioid-related hospital use as hospitalizations, and emergency department (ED) visits for a drug overdose, opioid abuse, and dependence. We defined tapering as a 15% or greater dose reduction following at least 3 months of continuous opioid therapy of 50 morphine milligram equivalents (MME)/day or more. We used conditional logistic regression to estimate odds ratios (OR) with 95% confidence intervals (CI). Recent tapering from LTOT was associated with increased odds of opioid-related hospital use (OR: 1.50, 95%CI: 1.34-1.63), ED visit (OR: 1.52; 95%CI: 1.35-1.72), and inpatient hospitalization (OR: 1.40; 95%CI: 1.20-1.65). We found no evidence of heterogeneity of the effect of tapering on opioid-related hospital use by gender, age, and race. Recent tapering among patients on a high baseline dose (>300 MME) was associated with increased odds of opioid-related hospital use (OR: 2.95, 95% CI: 2.12-4.11, p < 0.001) compared to patients on a lower baseline doses. Recent tapering from LTOT is associated with increased odds of opioid-related hospital use.

Planning for health information exchange: Perspectives of community pharmacists in the Community Pharmacy Enhanced Services Network of Indiana

BackgroundCommunity pharmacists are one of the most accessible health professionals and provide many different services. However, lack of access to complete patient information is a barrier to making meaningful patient interventions. ObjectivesTo determine (1) current and desired health information access among community pharmacists in the Community Pharmacy Enhanced Services Network (CPESN) of Indiana and (2) design considerations for a health information exchange tool for use by community pharmacists. MethodsThis voluntary study was conducted over an 8-day survey period in which 40 pharmacies within the CPESN Indiana network were contacted during regular business hours and asked to take part in a 15-minute telephone survey. Questions asked were informed by the following Consolidated Framework for Implementation Research intervention characteristics domain constructs: relative advantage, evidence strength and quality, adaptability, trialability, complexity, costs, and design quality and packaging. ResultsOf the 40 contacted pharmacies, 32 (80%) completed the survey. Most pharmacies reported access to immunization registry data; no other routine access was reported by any pharmacy. In questions assessing the relative advantage of Health Information Exchange (HIE) access compared with their current information access, at least 84.4% said that they agreed or strongly agreed with all statements. When choosing the data element most desirable to have access to via HIE in a community pharmacy, the most frequently selected choices were updated medication orders (n = 18, 56.3%), progress notes (n = 5, 15.6%), and laboratory tests (n = 4, 12.5%). Suggestions to improve ease of implementation included integration within dispensing software and clinical decision-making support features, such as alerts for pertinent lab values. ConclusionIntegrating HIE data into community pharmacies would provide community pharmacists with access to important patient data, and pharmacists believed that this would improve their practice. Future research should explore whether implementation of this type of tool leads to better patient outcomes and improved pharmacist job satisfaction.

Assessment of structured data elements for social risk factors.

Computable social risk factor phenotypes derived from routinely collected structured electronic health record (EHR) or health information exchange (HIE) data may represent a feasible and robust approach to measuring social factors. This study convened an expert panel to identify and assess the quality of individual EHR and HIE structured data elements that could be used as components in future computable social risk factor phenotypes. Technical expert panel. A 2-round Delphi technique included 17experts with an in-depth knowledge of available EHR and/or HIE data. The first-round identification sessions followed a nominal group approach to generate candidate data elements that may relate to socioeconomics, cultural context, social relationships, and community context. In the second-round survey, panelists rated each data element according to overall data quality and likelihood of systematic differences in quality across populations (ie, bias). Panelists identified a total of 89 structured data elements. About half of the data elements (n = 45) were related to socioeconomic characteristics. The panelists identified a diverse set of data elements. Elements used in reimbursement-related processes were generally rated as higher quality. Panelists noted that several data elements may be subject to implicit bias or reflect biased systems of care, which may limit their utility in measuring social factors. Routinely collected structured data within EHR and HIE systems may reflect patient social risk factors. Identifying and assessing available data elements serves as a foundational step toward developing future computable social factor phenotypes.

Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model.

New-onset heart failure (HF) is associated with poor prognosis and high healthcare utilization. Early identification of patients at increased risk incident-HF may allow for focused allocation of preventative care resources. Health information exchange (HIE) data span the entire spectrum of clinical care, but there are no HIE-based clinical decision support tools for diagnosis of incident-HF. We applied machine-learning methods to model the one-year risk of incident-HF from the Maine statewide-HIE. We included subjects aged ≥ 40 years without prior HF ICD9/10 codes during a three-year period from 2015 to 2018, and incident-HF defined as assignment of two outpatient or one inpatient code in a year. A tree-boosting algorithm was used to model the probability of incident-HF in year two from data collected in year one, and then validated in year three. 5,668 of 521,347 patients (1.09%) developed incident-HF in the validation cohort. In the validation cohort, the model c-statistic was 0.824 and at a clinically predetermined risk threshold, 10% of patients identified by the model developed incident-HF and 29% of all incident-HF cases in the state of Maine were identified. Utilizing machine learning modeling techniques on passively collected clinical HIE data, we developed and validated an incident-HF prediction tool that performs on par with other models that require proactively collected clinical data. Our algorithm could be integrated into other HIEs to leverage the EMR resources to provide individuals, systems, and payors with a risk stratification tool to allow for targeted resource allocation to reduce incident-HF disease burden on individuals and health care systems.

GPS Mobile Health Intervention Among People Experiencing Homelessness: Pre-Post Study.

BackgroundPeople experiencing homelessness are at risk for gaps in care after an emergency department (ED) or hospital visit, which leads to increased use, poor health outcomes, and high health care costs. Most people experiencing homelessness have a mobile phone of some type, which makes mobile health (mHealth) interventions a feasible way to connect a person experiencing homelessness with providers.ObjectiveThis study aims to investigate the accuracy, acceptability, and preliminary outcomes of a GPS-enabled mHealth (GPS-mHealth) intervention designed to alert community health paramedics when people experiencing homelessness are in the ED or hospital.MethodsThis study was a pre-post design with baseline and 4-month postenrollment assessments. People experiencing homelessness, taking at least 2 medications for chronic conditions, scoring at least 10 on the Patient Health Questionnaire-9, and having at least 2 ED or hospital visits in the previous 6 months were eligible. Participants were issued a study smartphone with a GPS app programmed to alert a community health paramedic when a participant entered an ED or hospital. For each alert, community health paramedics followed up via telephone to assess care coordination needs. Participants also received a daily email to assess medication adherence. GPS alerts were compared with ED and hospital data from the local health information exchange (HIE) to assess accuracy. Paired t tests compared scores on the Patient Health Questionnaire-9, Medical Outcomes Study Social Support Survey, and Adherence Starts with Knowledge-12 adherence survey at baseline and exit. Semistructured exit interviews examined the perceptions and benefits of the intervention.ResultsIn total, 30 participants were enrolled; the mean age was 44.1 (SD 9.7) years. Most participants were male (20/30, 67%), White (17/30, 57%), and not working (19/30, 63%). Only 19% (3/16) of the ED or hospital visit alerts aligned with HIE data, mainly because of patients not having the smartphone with them during the visit, the smartphone being off, and gaps in GPS technology. There was a significant difference in depressive symptoms between baseline (mean 16.9, SD 5.8) and exit (mean 12.7, SD 8.2; t19=2.9; P=.009) and a significant difference in adherence barriers between baseline (mean 2.4, SD 1.4) and exit (mean 1.5, SD 1.5; t17=2.47; P=.03). Participants agreed that the app was easy to use (mean 4.4/5, SD 1.0, with 5=strongly agree), and the email helped them remember to take their medications (mean 4.6/5, SD 0.6). Qualitative data indicated that unlimited smartphone access allowed participants to meet social needs and maintain contact with case managers, health care providers, family, and friends.ConclusionsmHealth interventions are acceptable to people experiencing homelessness. HIE data provided more accurate ED and hospital visit information; however, unlimited access to reliable communication provided benefits to participants beyond the study purpose of improving care coordination.

Abstract P065: Odds Of Heart Disease And Arrhythmia Associated With Exposure Dose To Homelessness

Background: Adults experiencing homelessness are at 40-50% greater mortality risk from cardiovascular disease (CVD) than the general population, however little is understood about how the duration or degree of homelessness increases the risk of CVD. The Vulnerability Index-Service Prioritization Decision Assistance Tool (VI-SPDAT v1, 2012) is a 50-item measure intended to proscribe appropriate levels of intervention and prioritize the waiting list for housing. The measure also includes an item to assess history of CVD. The objective of this study is to examine the relation between duration and frequency of homelessness and CVD diagnosis, while controlling for potential confounders. Method: Version 1 of the VI-SPDAT was used to assess a cohort of individuals experiencing homelessness (n=4,739 unique participants; median age=47, {17-88}) and seeking subsidized housing services in a single county in Texas between 2014 and 2017. Medical questions were validated through electronic, deterministic linkage to the Community’s Health Information Exchange (HIE) for indigent populations. Logistic regression models identified independent covariates and potential effect modifiers of a theoretical dose-response relationship between homelessness and CVD. Results: A history of heart disease or arrhythmia was reported in 24.98% of the sample. For participants with repeated assessments (n=713; median interval of homelessness: 306 days; IQR: 216-441 days) the prevalence increased from 25.6% to 30.6% (p&lt;0.05). Criterion validation of the tool using HIE data supported that this prevalence estimate was under-reported (38.67%; 26.44% adjusted for full sample). Odds of CVD increased with age (OR=1.03; 95%CI:1.02-1.04) and spending more than two years in homelessness (OR=1.45; 95%CI:1.27-1.67). Proportion of the sample with CVD was also higher for females, non-Hispanic participants (both p&lt;0.05), those reporting medication adherence issues, problems with concentration, history of traumatic brain injury, and mental health-related ED visits (all p&lt;0.001). Age at assessment (p&lt;0.001), total time spent homeless (p&lt;0.001), and recent episodes (p&lt;0.05) were all independently associated with CVD. Cognitive deficits and medication adherence issues mediated the relationship between multiple demographics, homeless episodes, and reported CVD diagnosis, but fit independently into a model with age, gender, and 2 years or more spent in homelessness. Discussion: This study presents a rare window into the complex interrelationships of the time-dose of exposure to homelessness and the concomitant social and medical vulnerabilities. Time spent in homelessness and the number of recent homelessness periods were independently associated with CVD, even while controlling for age, suggesting a complex dose-response relationship.

Clinical data sharing improves quality measurement and patient safety.

ObjectiveAccurate and robust quality measurement is critical to the future of value-based care. Having incomplete information when calculating quality measures can cause inaccuracies in reported patient outcomes. This research examines how quality calculations vary when using data from an individual electronic health record (EHR) and longitudinal data from a health information exchange (HIE) operating as a multisource registry for quality measurement. Materials and MethodsData were sampled from 53 healthcare organizations in 2018. Organizations represented both ambulatory care practices and health systems participating in the state of Kansas HIE. Fourteen ambulatory quality measures for 5300 patients were calculated using the data from an individual EHR source and contrasted to calculations when HIE data were added to locally recorded data.ResultsA total of 79% of patients received care at more than 1 facility during the 2018 calendar year. A total of 12 994 applicable quality measure calculations were compared using data from the originating organization vs longitudinal data from the HIE. A total of 15% of all quality measure calculations changed (P < .001) when including HIE data sources, affecting 19% of patients. Changes in quality measure calculations were observed across measures and organizations.DiscussionThese results demonstrate that quality measures calculated using single-site EHR data may be limited by incomplete information. Effective data sharing significantly changes quality calculations, which affect healthcare payments, patient safety, and care quality.ConclusionsFederal, state, and commercial programs that use quality measurement as part of reimbursement could promote more accurate and representative quality measurement through methods that increase clinical data sharing.

Assessing follow-up care compliance in children hospitalized for traumatic brain injuries

Background and Hypothesis: There is limited information regarding healthcare utilization and outcomes in children hospitalized for traumatic brain injury (TBI). Nearly 50% of adults hospitalized for trauma do not attend follow-up appointments, although completion of post-discharge care is associated with improved outcomes and decreased likelihood of subsequent emergency department (ED) visits. The Regestrief Institute Indiana Network for Patient Care (INPC) is a regional health information exchange (HIE) with health record data. This includes inpatient, outpatient, and ED visits, as well as imaging and lab data. The objective of this study is to use HIE data to assess long-term healthcare utilization, complications, and sequelae of pediatric patients hospitalized for TBI to see if follow-up compliance can identify patients at risk for post-TBI complications, including unplanned care, as well as long-term secondary health conditions. &#x0D; &#x0D; Methods: 387 patients treated at a pediatric level 1 trauma center in Indiana admitted for TBI were identified using trauma registry data. EHR data in the INPC on patients for two years post-discharged were analyzed. Associations between compliance with follow-up care instructions given at discharge/subsequent medical visits and longitudinal utilization/outcomes were examined using Fisher’s exact test. &#x0D; &#x0D; Results: After reviewing patient records, we found that 60.7% of patients received all follow-up care and 8.5% of patients received partial follow-up care, leaving 25.1% of patients receiving no follow up care and 5.7% of patients lost to follow-up after discharge. 12% of patients went to the ER for an injury-related issue and 6.2% of patients were readmitted. 19.4% of individuals experienced complications from injury while 12.4% of individuals had suspected sequela. Factors influencing recovery included race, age, insurance, injury severity, ICU admission, and ventilator usage. &#x0D; &#x0D; Implications and Importance: Using HIE data can identify factors of hospitalized children vulnerable to not achieving optimal recovery and determine what care is critical to improving long-term health and quality of life outcomes.

Converting clinical document architecture documents to the common data model for incorporating health information exchange data in observational health studies: CDA to CDM

Utilization of standard health information exchange (HIE) data is growing due to the high adoption rate and interoperability of electronic health record (EHR) systems. However, integration of HIE data into an EHR system is not yet fully adopted in clinical research. In addition, data quality should be verified for the secondary use of these data. Thus, the aims of this study were to convert referral documents in a Health Level 7 (HL7) clinical document architecture (CDA) to the common data model (CDM) to facilitate HIE data availability for longitudinal data analysis, and to identify data quality levels for application in future clinical studies. A total of 21,492 referral CDA documents accumulated for over 10years in a tertiary general hospital in South Korea were analyzed. To convert CDA documents to the Observational Medical Outcomes Partnership (OMOP) CDM, processes such as CDA parsing, data cleaning, standard vocabulary mapping, CDA-to-CDM mapping, and CDM conversion were performed. The quality of CDM data was then evaluated using the Achilles Heel and visualized with the Achilles tool. Mapping five CDA elements (document header, problem, medication, laboratory, and procedure) into an OMOP CDM table resulted in population of 9 CDM tables (person, visit_occurrence, condition_occurrence, drug_exposure, measurement, observation, procedure_occurrence, care_site, and provider). Three CDM tables (drug_era, condition_era, and observation_period) were derived from the converted table. From vocabulary mapping codes in CDA documents according to domain, 98.6% of conditions, 68.8% of drugs, 35.7% of measurements, 100% of observation, and 56.4% of procedures were mapped as standard concepts. The conversion rates of the CDA to the OMOP CDM were 96.3% for conditions, 97.2% for drug exposure, 98.1% for procedure occurrence, 55.1% for measurements, and 100% for observation. We examined the possibility of CDM conversion by defining mapping rules for CDA-to-CDM conversion using the referral CDA documents collected from clinics in actual medical practice. Although mapping standard vocabulary for CDM conversion requires further improvement, the conversion could facilitate further research on the usage patterns of medical resources and referral patterns.

Leveraging health information exchange for clinical research: Extreme underreporting of hospital service utilization among patients with substance use disorders

BackgroundSubstance use disorders are associated with inefficient and fragmented use of healthcare services. The Chesapeake Regional Information System for Our Patients, Inc. (CRISP) is a Health Information Exchange (HIE) linking disparate systems of care in the mid-Atlantic region. MethodsThis article describes applications of HIE for tracking hospital service utilization in substance use disorder clinical and services research, drawing upon data from one of the first studies approved to access the CRISP HIE. Participants were 200 medical/surgical inpatients with comorbid opioid, cocaine, and/or alcohol use disorder (45.5 % female; 56.5 % black; 77.5 % opioid use disorder; 42.0 % homeless). This study compared HIE-identified hospital service utilization with conventional methods of participant self-report during in-person research follow-ups (3-, 6-, and 12-months post-discharge) and electronic health record (EHR) review from the hospital system of the index admission. ResultsThis sample exhibited high levels of hospital utilization, which would have been underestimated using conventional methods. Relying exclusively on self-report in the 12-month observation period would have identified only 33.8 % of 429 inpatient hospitalizations and 9.0 % of 1,287 ED visits, due to both loss-to-follow-up and failure to report events. Even combining self-report with single-system EHR review identified only 66.2 % of inpatient hospitalizations and 59.8 % of ED visits. ConclusionsCRISP HIE data were superior to conventional methods for ascertaining hospital service utilization in this sample of patients exhibiting high-volume and fragmented care. The use of HIE holds implications for improving rigor, safety, and efficiency in research studies.

Tele-transitions of care (TTOC): a 12-month, randomized controlled trial evaluating the use of Telehealth to achieve triple aim objectives

BackgroundPoor transitions of care leads to increased health costs, over-utilization of emergency room departments, increased re-hospitalizations and causes poor patient experiences and outcomes. This study evaluated Telehealth feasibility in improving transitions of care.MethodsThis is a 12-month randomized controlled trial, evaluating the use of telehealth (remote patient monitoring and video visits) versus standard transitions of care with the primary outcomes of hospital readmission and emergency department utilization and secondary outcomes of access to care, medication management and adherence and patient engagement. Electronic Medical Record data, Health Information Exchange data and phone survey data was collected. Multi-variable logistic regression models were created to evaluate the effect of Telehealth on hospital readmission, emergency department utilization, medication adherence. Chi-square tests or Fisher’s exact tests were used to compare the percentages of categorical variables between the Telehealth and control groups. T tests or Wilcoxon rank sum tests were used to compared means and medians between the two randomized groups.ResultsThe study conducted between June 2017 and 2018, included 102 patients. Compared with the standard of care, Telehealth patients were more likely to have medicine reconciliation (p = 0.013) and were 7 times more likely to adhere to medication than the control group (p = 0.03). Telehealth patients exhibited enthusiasm (p = 0.0001), and confidence that Telehealth could improve their healthcare (p = 0.0001). Telehealth showed no statistical significance on emergency department utilization (p = 0.691) nor for readmissions (p = 0.31). 100% of Telehealth patients found the intervention to be valuable, 98% if given the opportunity, reported they would continue using telehealth to manage their healthcare needs, and 94% reported that the remote patient monitoring technology was useful.ConclusionsTelehealth can improve transitions of care after hospital discharge improving patient engagement and adherence to medications. Although this study was unable to show the effect of Telehealth on reduced healthcare utilization, more research needs to be done in order to understand the true impact of Telehealth on preventing avoidable hospital readmission and emergency department visits.Trial registrationClinicalTrials.Gov ID: NCT03528850 Date Registered (Retrospective): 5/18/2018.Status: Completed.IRB #: 970227.

Opioid-Related Emergency Department Encounters: Patient, Encounter, and Community Characteristics Associated With Repeated Encounters

We describe the prevalence, trends, and factors associated with repeated emergency department (ED) encounters for opioid usage across multiple, independent hospital systems. A statewide regional health information exchange system provided ED encounters from 4 Indiana hospital systems for 2012 to 2017. In accordance with a series of International Classification of Diseases, Ninth Revision (ICD-9) and ICD-10 diagnosis codes for opioid abuse, adverse effects of opioids, opioid dependence and unspecified use, and opioid poisoning, we identified patients with an ED encounter associated with opioid usage (9,295 individuals; 12,642 encounters). Multivariate logistic regression models then described the patient, encounter, prescription history, and community characteristics associated with the odds of a patient's incurring a subsequent opioid-related ED encounter. The prevalence of repeated nonfirst opioid-related ED encounters increased from 9.0% of all opioid encounters in 2012 to 34.3% in 2017. The number of previous opioid-related ED encounters, unique institutions at which the individual had had encounters, the encounter's being heroin-related, the individual's having a benzodiazepine prescription filled within 30 days before the encounter, and being either Medicaid insured or uninsured compared with private insurance were associated with significantly greater odds of having a subsequent encounter. The ED is increasingly a site utilized as the setting for repeated opioid-related care. Characteristics of the individual, encounter, and community associated with repeated opioid-related encounters may inform real-time risk-prediction tools in the ED setting. Additionally, the number of institutions to which the individual has presented may suggest the utility of health information exchange data and usage in the ED setting.

Long-term prescription opioid utilization, substance use disorders, and opioid overdoses after adolescent trauma.

Injured adolescents have a 56% increased risk of developing a substance use disorder (SUD) within 3 years of their injury. The transition from medical prescription opioid use to nonmedical use in adolescent trauma patients has not been longitudinally studied long-term. The aim of this study is to describe 5-year patterns of opioid use in a cohort of injured adolescents as well as the proportion of patients experiencing overdose and SUD diagnoses. Our retrospective cohort study consisted of 736 patients aged 12 years to 18 years who were admitted for trauma from 2011 to 2013. We examined up to 5 years of regional health information exchange data containing information on prescription fills as well as diagnoses from inpatient, outpatient, and emergency department encounters. At 1 year, over 20% of adolescents filled more than two opioid prescriptions after being discharged for their injury; and at 4 years, over 13% had received more than eight opioid fills. Over the 5-year period, 11% received an opioid antagonist injection, 14% received an SUD diagnosis, and 8% had an overdose diagnosis. Relatively few patients had diagnoses for other mental health conditions including depression (5.5%), posttraumatic stress disorder (2.1%), and chronic pain (3.6%). Opioid usage remains high for multiple years in a subset of the adolescent trauma population. Mental health diagnosis rates were substantially lower in injured adolescents than what has been reported in adults. However, overdose and SUD diagnoses occur in over 1 in 10 adolescents within 5 years of their injury. Prognostic and epidemiological study, level IV.