Arizona Health Information Exchange

abstract: Arizona strives to be the national role model for the secure, interoperable health information exchange to facilitate safe, secure, high quality and cost effective health care. The purpose of the Health Information Exchange in Arizona is to improve the quality, safety and efficiency of wellness in the Arizona population by securely connecting patients and health care providers so that relevant and understandable information is available anytime, anywhere

Impact of health information exchange adoption on referral patterns

Efforts to promote Health Information Exchanges (HIE) on a nationwide scale are beset with major challenges and one of them is its meaningful use for both physicians and patients. Referrals potentially provide a context for the meaningful use of HIE and we are yet to understand how HIEs affect referrals. This research studies the impact of HIE on referral patterns. We establish that participation in an HIE network increases the referrals sent to and received from other HIE participants. We investigate this relationship using both econometric and network-analytic methods. While the econometric analysis focuses on the underlying associations between HIE adoption and referral patterns, the network analysis addresses the transformation process by which HIE adoption and referrals co-evolve over time. This study has significant implications for healthcare policy-making, development of innovative HIE business models, and management of healthcare organizations

Malaysia health information exchange: a systematic review

Malaysia Health Information Exchange (MyHiX) is part of Health Transformation System, which is a national agenda to improve the healthcare service using ICT as an enabler. Its main focus is to share patients’ discharge summary or clinical summary among healthcare facilities. This initiative was launched in November 2008. However, nine years after its launch, MyHiX has been successfully implemented only in eight healthcare institutions in Malaysia. Therefore, this paper presents a systematic review of the existing literatures related to Health Information Exchange or MyHiX, with the aim of exploring the empirical gaps and issues associated with its implementation. Six databases were used to locate relevant studies, namely IEEE, Proquest, Emerald, Science Direct, Google Scholar and Scopus. In total, 70 studies met the inclusion criteria. Further thematic analysis of these studies revealed that issues of MyHIX implementation could be categorized into technological, organizational, environmental and human contexts

Health Information Exchange Use in Primary Care

Indiana University-Purdue University Indianapolis (IUPUI)The United States has invested over $40 billion in digitizing the health care system, yet the anticipated gains in improved care coordination, quality, and cost savings remain largely unrealized. This is due in part to limited interoperability and low rates of health information exchange (HIE) use, which can support care coordination and improve provider decision-making. Primary care providers are central to the US health care delivery system and frequently function as care coordinators, yet capability and HIE use gaps among these providers limit the potential of these digital systems to achieve their intended goals. I study HIE use in the context of primary care to examine 1) factors associated with provider HIE use, 2) the extent and nature of team-based HIE use, and 3) differences in HIE system use patterns across discrete groups of system users. First, I use a national sample of primary care providers to analyze market and practice factors related to HIE use for patient referrals. Overall, I find that only 43% of primary care provider referrals used HIE. Furthermore, I find substantial variation in HIE use rates across electronic health record (EHR) vendors. Second, I use HIE system log data to understand the breadth and depth of HIE use among teams, a care model underpinning primary care delivery reform efforts. I find that although use of HIE systems remains low, in primary care settings it overwhelmingly takes place in a manner consistent with team-based care workflows. Furthermore, team-based use does not differ in breadth from single provider HIE use, but illustrates less depth before and after visits. Third, I apply cluster analysis to 16 HIE use measures representing 7 use attributes, and identify 5 discrete user groups. I then compare two of these user groups and find user-level variation in volume and efficiency of use, both of which have implications for HIE system design and usability improvements. Ultimately, these findings help to inform how HIE use can be increased and improved in primary care, moving the US health care system closer to realizing the coordination, quality, and cost savings made possible by a digitized delivery system

Health Information Exchange: Growth and Patient Privacy

Health Information Exchanges (HIE) provide the electronic movement of health-related information among organizations according to nationally recognized standards. The goal of health information exchange is to facilitate access to and retrieval of clinical data to provide safer, timelier, efficient, effective, equitable, patient-centered care. HIEs are becoming integral parts of the national healthcare reform efforts, chiefly owing to their potential impact on cost reduction and quality enhancement in healthcare services. However, the potential of a HIE platform can only be realized when its multiple constituent users actively participate in using its variety of services. In this research, Yaraghi models HIE systems as multisided platforms that incorporate self-service technologies whose value to the users depends on both user-specific and network-specific factors. Yaraghi also will discuss patient privacy on HIE systems and show the effect of the emotional and environmental factors on the patients’ decision to disclose their medical information on HIE systems

Health Information Exchange, Interoperability, and Network Effects

Health information exchange (HIE) is the electronic exchange of patient medical records among hospitals. I investigate how two defining characteristics of HIE can cause under-adoption. First, HIE represents an information-sharing network, and participation for any hospital is valuable if others participate. This is a network effect. Second, HIE involves exchange of medical records, which may be competitive assets for hospitals. Therefore, hospitals may have disincentive to exchange with competitors despite social benefits. This is a competitive effect. I present a theoretical framework of hospitalsâ?? decisions to adopt HIE and show how presence of network and competitive effects can result in under-adoption relative to the social optimal. I then test for evidence of network and competitive effects in hospital HIE adoption. In the empirical analysis, I use two measure of HIE adoption. The first is a measure of general adoption in which all hospitals that have adopted any HIE capability are assumed to be able to exchange information with each other. The second takes into account that much information-sharing occurs through interoperable IT systems and currently, most IT systems of different vendors are not interoperable. I find evidence of network effects in general HIE adoption and vendor choice. I find that a 10% increase in market adoption rate results in a hospital being 9.2% more likely to adopt HIE. I also find that a 10% increase in adoption rate of a vendor results in a hospital being 1.5% more likely to adopt the vendor. I also find evidence of competitive effects. Specifically, hospitals that are more vulnerable to losing market share are less likely to adopt a prominent vendor in a market. I estimate a model of patientsâ?? hospital preferences and hospitalsâ?? HIE adoption decision. Through counterfactual simulation, I show effects of widespread HIE adoption on market share redistribution. Finally, I evaluate current policies such as the federal governmentâ??s $30 billion adoption incentive program (HITECH Act). The program may be inadequate to promote widespread interoperability in the presence of competitive effects. I also discuss the implications of network effects for competition and innovation in the health IT industry

Machine Learning applied to health information exchange

The interest in artificial intelligence (AI) has grown in the last few years. The healthcare community is no exception. The present work is focused on the exchange of medical information, using the Health Level Seven (HL7) international standards. The main objective of the present work is to develop an AI model capable of inferring if for a given hour exists a peak in the number of exchanged messages. To accomplish that, two different deep learning models were created, an artificial neural networks (ANN) and long short-term memory (LSTM). The intention is to observe which is capable to perceive the situation better considering the environment and features of a healthcare facility. Using laboratory-generated data, it was possible to simulate variations and differences in “traffic.” Comparing the LSTM vs. ANN model, the first is capable of outputting peaks better but for considered mean values that do not happen. For the given context, predicting the peak is essential, so the LSTM is the right choice and uses fewer features that are good regarding performance.- (undefined

The implications and impact of 3 approaches to health information exchange: community, enterprise, and vendor‐mediated health information exchange

IntroductionElectronic health information exchange (HIE) is considered essential to establishing a learning health system, reducing medical errors, and improving efficiency, but establishment of widespread, high functioning HIE has been challenging. Healthcare organizations now have considerable flexibility in selecting among several HIE strategies, most prominently community HIE, enterprise HIE (led by a healthcare organization), and electronic health record vendor‐mediated HIE. Each of these strategies is characterized by different conveners, capabilities, and motivations and may have different abilities to facilitate improved patient care.MethodsI reviewed the available scholarly literature to draw conceptual distinctions between these types of HIE, to assess the current evidence on each type of HIE, and to indicate important areas of future research.ResultsWhile community HIE seems to offer the most open approach to HIE allowing for high levels of connectivity, both enterprise HIE and vendor‐mediated HIE face lower barriers to formation and sustainability. Most existing evidence is focused on community HIE and points towards low overall use, challenges to usability, and ambiguous impact. To better guide organizational leaders and policymakers in the expansion of beneficial HIE and anticipate future trends, future research should work to better capture the prevalence of other forms of HIE, and to adopt common methods to allow comparisons of rate of use, usability, and impact on patient care across studies and types of HIE.ConclusionsHealthcare organizations’ choice of HIE strategy influences the set of partners the organization is connected to and may influence the benefit that efforts supported by HIE can offer to patients. Current research is not fully capturing the diversity of approaches to HIE and their potentially varying impact on providers and patients.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136725/1/lrh210021_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136725/2/lrh210021.pd

Evaluating public health uses of health information exchange

AbstractHealth information exchange (HIE) initiatives are in various stages of development across the United States. They aim to bring previously unavailable clinical data from patients’ disparate health records, which may be spread over multiple provider and payer networks, to the point of care where clinicians and their patients need it most. The implications of these initiatives on public health are numerous. This article provides general evaluation methods for measuring the impact of HIE on public health in six use cases: (1) mandated reporting of laboratory diagnoses, (2) mandated reporting of physician-based diagnoses, (3) public health investigation, (4) disease-based non-reportable laboratory data, (5) antibiotic-resistant organism surveillance, and (6) population-level quality monitoring