Health Information Exchange in Emergency Medicine

Shapiro, Jason S.; Crowley, Diana; Hoxhaj, S.; Langabeer, James R.; Panik, Brian; Taylor, Todd B.; Weltge, Arlo; Nielson, Jeffrey

doi:10.1016/j.annemergmed.2015.06.018

Cited by 31 publications

(24 citation statements)

References 38 publications

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“…This led to a much greater enrollment than an “opt-in” methodology, which requires patients to provide separate consent to be included in the HIE. 23 In an opt-in situation participation tends to drop precipitously. No one opted out of our HIE during the study period, making it a comprehensive dataset that included every patient who visited an ED in our region during the study period.…”

mentioning

confidence: 99%

A Comprehensive View of Frequent Emergency Department Users Based on Data from a Regional HIE

Saef¹,

Carr²,

Bush³

et al. 2016

Southern Medical Journal

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Objectives A small but significant number of patients make frequent emergency department (ED) visits to multiple EDs within a region. We have a unique health information exchange (HIE) that includes every ED encounter in all hospital systems in our region. Using our HIE we were able to characterize all frequent ED users in our region, regardless of hospital visited or payer class. The objective of our study was to use data from an HIE to characterize patients in a region who are frequent ED users (FEDUs). Methods We constructed a database from a cohort of adult patients (18 years old or older) with information in a regional HIE for a 1-year period beginning March 2012.A Patients were defined as FEDUs (those who made four or more visits during the study period) and non-FEDUs (those who made fewer than four ED visits during the study period). Predictor variables included age, race, sex, payer class, county of residence, and International Classification of Diseases, Ninth Revision (ICD-9) codes. Bivariate (χ2) and multivariate (logistic regression) analyses were performed to determine associations between predictor variables and the outcome of being an FEDU. Results The database contained 127,672 patients, 12,293 (9.6%) of whom were FEDUs. Logistic regression showed the following patient characteristics to be significantly associated with the outcome of being an FEDU: age 35 to 44 years; African American race; Medicaid, Medicare, and dual-pay payer class; and ICD-9 codes 630 to 679 (complications of pregnancy, childbirth, and puerperium), 780 to 799 (ill-defined conditions), 280 to 289 (diseases of the blood), 290–319 (mental disorders), 680 to 709 (diseases of the skin and subcutaneous tissue), 710 to 739 (musculoskeletal and connective tissueB disease), 460 to 519 (respiratory disease), and 520 to 579 (digestive disease). No significant differences were noted between men and women. Conclusions Data from an HIE can be used to describe all of the patients within a region who are FEDUs, regardless of the hospital system they visited. This information can be used to focus care coordination efforts and link appropriate patients to a medical home. Future studies can be designed to learn the reasons why patients become FEDUs, and interventions can be developed to address deficiencies in health care that result in frequent ED visits.

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mentioning

confidence: 99%

A Comprehensive View of Frequent Emergency Department Users Based on Data from a Regional HIE

Saef¹,

Carr²,

Bush³

et al. 2016

Southern Medical Journal

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“…CeHA had the advantage of being designed as an “opt-out” HIE, a mechanism of enrollment that was recommended as the preferred form of HIE enrollment by Shapiro et al in a position paper developed in response to an American College of Emergency Physicians resolution. 12 During the study period no patients opted out of participating in our HIE, resulting in a comprehensive database that included all of the patients who visited any ED in our region. These data were made available to the researchers to examine the implementation, use, and effectiveness of the local eHealth exchange.…”

Section: Methodsmentioning

confidence: 99%

When Should ED Physicians Use an HIE? Predicting Presence of Patient Data in an HIE

Carr¹,

Saef²,

Zhang³

et al. 2016

Southern Medical Journal

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Objectives Health information exchanges (HIEs) make possible the construction of databases to characterize patients as multisystem users (MSUs), those visiting emergency departments (EDs) of more than one hospital system within a region during a 1-year period. HIE data can inform an algorithm highlighting patients for whom information is more likely to be present in the HIE, leading to a higher yield HIE experience for ED clinicians and incentivizing their adoption of HIE. Our objective was to describe patient characteristics that determine which ED patients are likely to be MSUs and therefore have information in an HIE, thereby improving the efficacy of HIE use and increasing ED clinician perception of HIE benefit. Methods Data were extracted from a regional HIE involving four hospital systems (11 EDs) in the Charleston, South Carolina area. We used univariate and multivariable regression analyses to develop a predictive model for MSU status. Results Factors associated with MSUs included younger age groups, dual-payer insurance status, living in counties that are more rural, and one of at least six specific diagnoses: mental disorders; symptoms, signs, and ill-defined conditions; complications of pregnancy, childbirth, and puerperium; diseases of musculoskeletal system; injury and poisoning; and diseases of the blood and blood-forming organs. For patients with multiple ED visits during 1 year, 43.8% of MSUs had ≥4 visits, compared with 18.0% of non-MSUs (P < 0.0001). Conclusions This predictive model accurately identified patients cared for at multiple hospital systems and can be used to increase the likelihood that time spent logging on to the HIE will be a value-added effort for emergency physicians.

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“…HEPMA systems were chosen as a timely exemplar to explore issues related to wider health information infrastructures and associated strategy to create learning health systems 18 , 19…”

Section: Introductionmentioning

confidence: 99%

Establishing data-intensive healthcare: the case of Hospital Electronic Prescribing and Medicines Administration systems in Scotland

Cresswell

Smith

Swainson

et al. 2016

jhi

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Background Creating learning health systems, characterised by the use and repeated reuse of demographic, process and clinical data to improve the safety, quality and efficiency of care, is a key aim in realising the potential benefits and efficiency savings associated with the implementation of health information technology. Objectives We sought to investigate stakeholder perspectives on and experiences of the implementation of hospital electronic prescribing and medicines administration (HEPMA) systems in Scotland and use these to inform political decisions on approaches to promoting the use and reuse of digitised prescribing and medication administration data in order to improve care processes and outcomes. Methods We identified and recruited key national stakeholders involved in implementing and/or using HEPMA data from generic and specialty systems. These included representatives from healthcare settings (i.e. doctors, pharmacists and nurses), managers of existing national databases, policy makers, healthcare analytics companies, system suppliers and patient representatives. We conducted multi-disciplinary focus group discussions, audio-recorded these, transcribed data verbatim and thematically analysed the transcripts with the help of NVivo10. In analysing the data, we drew on theoretical and previous empirical work on information infrastructures. Results We identified the following key themes: 1) micro-factors -usability of systems and motivating users to input data; 2) meso-factors -developing technical and organisational infrastructures to facilitate the aggregation of data; and 3) macro-factors -facilitating interoperability and data reuse at larger scales to ensure that data are effectively generated and used.

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Health Information Exchange in Emergency Medicine

Cited by 31 publications

References 38 publications

A Comprehensive View of Frequent Emergency Department Users Based on Data from a Regional HIE

A Comprehensive View of Frequent Emergency Department Users Based on Data from a Regional HIE

When Should ED Physicians Use an HIE? Predicting Presence of Patient Data in an HIE

Establishing data-intensive healthcare: the case of Hospital Electronic Prescribing and Medicines Administration systems in Scotland

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