Investment In Health Information Technology Research Articles

Tales from the Frontline: Implementing an E-Prescription Technology

Health Information Technology (HIT) has great potential to enhance patient care and improve efficiency of health care delivery if it is appropriately designed, implemented and used. In 2009 the Office of the National Coordinator (ONC) created Meaningful Use (MU) incentives to promote HIT adoption, which has spurred major investment in HIT, but the program has shown no reduction in use errors. Because the MU program mainly focuses on clinical process measures, with no attention to the processes of system design and implementation, many organizations encounter challenges to attain the MU. Previous studies identified lack of adequate implementation plans, lack of team support, lack of workflow analysis, and lack of dedicated resources as main barriers to MU attainment. We have identified similar concerns, and here present tales from the frontline to show perceptions of end users when our organization rushed through the implementation of e-prescription technology to meet the MU deadlines. Insufficient attention to the design and implementation process has put our organizations at risk. Reactive mitigation solutions were put in place to counteract the identifiable potential risks.

Factors Affecting Health Information Technology Expenditure in California Hospitals

This paper investigates the factors affecting health information technology (IT) investment. Different from previous studies, health IT was measured as the dollar amount of hardware, software and labor related health IT. This study employed Hospital and Patient level data of the Office of Statewide Health Planning and Development (OSHPD) from 2000 to 2006. The generalized linear model (GLM) was employed with log link and normal distribution and controlled for clustering error. This study found that not-for-profit and government hospital, teaching hospitals, competition, health IT expenditure of neighborhood hospitals were positively associated with health IT expenditure. However, rural hospitals were negatively associated with health IT expenditure. Moreover, this study found a significant increase in health IT investment over seven years resulted from increased clinical IT adoption.

The impact of health information technology on disparity of process of care.

IntroductionDisparities in the quality of health care and treatment among racial or ethnic groups can result from unequal access to medical care, disparate treatments for similar severities of symptoms, and wide divergence in general health status among individuals. Such disparities may be eliminated through better use of health information technology (IT). Investment in health IT could foster better coordinated care, improve guideline compliance, and reduce the likelihood of redundant testing, thereby encouraging more equitable treatment for underprivileged populations. However, there is little research exploring the impact of health IT investment on disparities of process of care.MethodologyThis study examines the impact of health IT investment on waiting times – from admission to the date of first principle procedure – among different racial and ethnic groups, using patient and hospital data for the state of California collected from 2001 to 2007. The final sample includes 14,056,930 patients admitted with medical diseases to 316 unique, acute-care hospitals over a seven-year period. The linear random intercept and slope model was employed to examine the impacts of health IT investment on waiting time, while controlling for patient, disease, and hospital characteristics.ResultsGreater health IT investment was associated with shorter waiting times, and the reduction in waiting times was greater for non-White than for White patients. This indicates that minority populations could benefit from health IT investment with regard to process of care.ConclusionInvestments in health IT may reduce disparities in process of care.Electronic supplementary materialThe online version of this article (doi:10.1186/s12939-015-0161-3) contains supplementary material, which is available to authorized users.

Effects of health information technology on malpractice insurance premiums.

ObjectivesThe widespread adoption of health information technology (IT) will help contain health care costs by decreasing inefficiencies in healthcare delivery. Theoretically, health IT could lower hospitals' malpractice insurance premiums (MIPs) and improve the quality of care by reducing the number and size of malpractice. This study examines the relationship between health IT investment and MIP using California hospital data from 2006 to 2007.MethodsTo examine the effect of hospital IT on malpractice insurance expense, a generalized estimating equation (GEE) was employed.ResultsIt was found that health IT investment was not negatively associated with MIP. Health IT was reported to reduce medical error and improve efficiency. Thus, it may reduce malpractice claims from patients, which will reduce malpractice insurance expenses for hospitals. However, health IT adoption could lead to increases in MIPs. For example, we expect increases in MIPs of about 1.2% and 1.5%, respectively, when health IT and labor increase by 10%.ConclusionsThis study examined the effect of health IT investment on MIPs controlling other hospital and market, and volume characteristics. Against our expectation, we found that health IT investment was not negatively associated with MIP. There may be some possible reasons that the real effect of health IT on MIPs was not observed; barriers including communication problems among health ITs, shorter sample period, lower IT investment, and lack of a quality of care measure as a moderating variable.

Health IT and Ambulatory Care Quality

US government investments in health information technology (IT) have focused on giving incentives for digital health records in hospital settings and by individual physicians. We evaluate the omission of ambulatory care centers, by studying the effects of healthcare IT on ambulatory care quality, which we measure using the rate of hospital admissions for conditions identified as sensitive to ambulatory care quality, using data from Medicare and the Nationwide Inpatient Sample. Results from difference-indifferences models that control for location and time fixed effects, as well as observable factors related to healthcare quality and population demographics, indicate that increased ambulatory IT adoption lowers local area ambulatory care sensitive (ACS) hospitalizations, suggesting quality improvements. The magnitudes imply that a 45% increase in ambulatory IT adoption in a county (the average increase over our sample period 2003-2012) lowers the ACS admission rate in that county by about 1.6%.

Beacon communities' public health initiatives: a case study analysis.

Introduction:The Beacon Communities for Public Health (BCPH) project was launched in 2011 to gain a better understanding of the range of activities currently being conducted in population- and public health by the Beacon Communities. The project highlighted the successes and challenges of these efforts with the aim of sharing this information broadly among the public health community.Background:The Beacon Community Program, designed to showcase technology-enabled, community-based initiatives to improve outcomes, focused on: building and strengthening health information technology (IT) infrastructure and exchange capabilities; translating investments in health IT to measureable improvements in cost, quality, and population health; and, developing innovative approaches to performance measurement, technology, and care delivery.Methods:Four multimethod case studies were conducted based on a modified sociotechnical framework to learn more about public health initiative implementation and use in the Beacon Communities. Our methodological approach included using document review and semistructured key informant interviews. NACCHO Model Practice Program criteria were used to select the public health initiatives included in the case studies.Findings:Despite differences among the case studies, common barriers and facilitators were found to be present in all areas of the sociotechnical framework application including structure, people, technology, tasks, overarching considerations, and sustainability. Overall, there were many more facilitators (range = 7–14) present for each Beacon compared to barriers (range = 4–6).Discussion:Four influential promising practices were identified through the work: forging strong and sustainable partnerships; ensuring a good task-technology fit and a flexible and iterative design; fostering technology acceptance; and, providing education and demonstrating value.Conclusions:A common weakness was the lack of a framework or model for the Beacon Communities evaluation work. Sharing a framework or approach to evaluation at the beginning of implementation made the work more effective. Supporting evaluation to inform future implementations is important.

Re-examining health IT policy: what will it take to derive value from our investment?

Despite substantial investments in health information technology (HIT), the nation's goals of reducing cost and improving outcomes through HIT remain elusive. This period of transition, with new Office of National Coordinator for HIT leadership, upcoming Meaningful Use Stage III definitions, and increasing congressional oversight, is opportune to consider needed course corrections in HIT strategy. This article describes current problems and recommended changes in HIT policy, including approaches to usability, interoperability, and quality measurement. Recommendations refrain from interim measures, such as electronic health record adoption rates, and instead focus on measurable national value to benefit the economy, to reduce healthcare costs, and to improve clinical efficiency and care quality.

Rapid learning: a breakthrough agenda.

A "rapid-learning health system" was proposed in a 2007 thematic issue of Health Affairs. The system was envisioned as one that uses evidence-based medicine to quickly determine the best possible treatments for patients. It does so by drawing on electronic health records and the power of big data to access large volumes of information from a variety of sources at high speed. The foundation for a rapid-learning health system was laid during 2007-13 by workshops, policy papers, large public investments in databases and research programs, and developing learning systems. Challenges now include implementing a new clinical research system with several hundred million patients, modernizing clinical trials and registries, devising and funding research on national priorities, and analyzing genetic and other factors that influence diseases and responses to treatment. Next steps also should aim to improve comparative effectiveness research; build on investments in health information technology to standardize handling of genetic information and support information exchange through apps and software modules; and develop new tools, data, and information for clinical decision support. Further advances will require commitment, leadership, and public-private and global collaboration.

Adoption and use of electronic health records among federally qualified health centers grew substantially during 2010-12.

Federally qualified health centers play an important role in providing health care to underserved populations. Recent substantial federal investments in health information technology have enabled health centers to expand their use of electronic health record (EHR) systems, but factors associated with adoption are not clear. We examined 2010-12 administrative data from the Health Resources and Services Administration's Uniform Data System for more than 1,100 health centers. We found that in 2012 nine out of ten health centers had adopted a EHR system, and half had adopted EHRs with basic capabilities. Seven in ten health centers reported that their providers were receiving meaningful-use incentive payments from the Centers for Medicare and Medicaid Services (CMS). Only one-third of health centers had EHR systems that could meet CMS's stage 1 meaningful-use core requirements. Health centers that met the stage 1 requirements had more than twice the odds of receiving quality recognition, compared with centers with less than basic EHRs. Policy initiatives should focus assistance on EHR capabilities with slower uptake; connect providers with technical assistance to support implementation; and leverage the connection between meaningful use and quality recognition programs.

Clinical informatics: prospects for a new medical subspecialty.

Only a few years ago, the mention of informatics in clinical circles generated questions regarding the rigor or relevance of the field. With the expanding interest and investment in health information technology by hospitals, health systems, and practitioners, however, interest in and acceptance of clinical informatics has increased substantially. Since 1972, the National Institutes of Health, principally through the National Library of Medicine (NLM), has supported a number of centers of excellence that focus on workforce education in computer applications and the underlying science. Additional efforts to help ensure a supply of competently trained individuals capable of maintaining progress with respect to applied clinical informatics are a recent development.

Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): architecture.

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative ‘apps’ to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.

A Review of Analytics and Clinical Informatics in Health Care

Federal investment in health information technology has incentivized the adoption of electronic health record systems by physicians and health care organizations; the result has been a massive rise in the collection of patient data in electronic form (i.e. "Big Data"). Health care systems have leveraged Big Data for quality and performance improvements using analytics-the systematic use of data combined with quantitative as well as qualitative analysis to make decisions. Analytics have been utilized in various aspects of health care including predictive risk assessment, clinical decision support, home health monitoring, finance, and resource allocation. Visual analytics is one example of an analytics technique with an array of health care and research applications that are well described in the literature. The proliferation of Big Data and analytics in health care has spawned a growing demand for clinical informatics professionals who can bridge the gap between the medical and information sciences.

Creating value: unifying silos into public health business intelligence.

Introduction:Through September 2014, federal investments in health information technology have been unprecedented, with more than 25 billion dollars in incentive funds distributed to eligible hospitals and providers. Over 85 percent of eligible United States hospitals and 60 percent of eligible providers have used certified electronic health record (EHR) technology and received Meaningful Use incentive funds (HITECH Act1).Technology:Certified EHR technology could create new public health (PH) value through novel and rapidly evolving data-use opportunities, never before experienced by PH. The long-standing “silo” approach to funding has fragmented PH programs and departments,2 but the components for integrated business intelligence (i.e., tools and applications to help users make informed decisions) and maximally reuse data are available now.Systems:Challenges faced by PH agencies on the road to integration are plentiful, but an emphasis on PH systems and services research (PHSSR) may identify gaps and solutions for the PH community to address.Conclusion:Technology and system approaches to leverage this information explosion to support a transformed health care system and population health are proposed. By optimizing this information opportunity, PH can play a greater role in the learning health system.

Health information exchange among U.S. hospitals: who's in, who's out, and why?

BackgroundA key goal of the 2009 HITECH Act is to ensure broad electronic exchange of clinical data among providers. We sought to assess whether current policy efforts, many of which are being developed by states, appear to be tackling key barriers to hospital participation in health information exchange (HIE). MethodsWe used the most recent national data from the American Hospital Association's IT Supplement to assess U.S. hospital participation in HIE and how participation varies by state. We then examined whether HIE is being pursued by all types of hospitals, or whether specific types of hospitals are not yet engaged. We focused on for-profit hospitals, those with smaller market share, and those in more competitive markets. ResultsWe found that 30% of U.S. hospitals engaged in health information exchange with unaffiliated providers. There was large variation in state-level participation, with some states achieving more than 70% participation (Rhode Island, Delaware and Vermont) and others with minimal participation. In markets where exchange occurred, for-profit hospitals were far less likely to engage in HIE than non-profit hospitals (OR=0.17; p<0.001). Hospitals with a larger market share were more likely to engage in exchange (OR=2.05 for hospitals in the highest relative to the lowest quartile of market share; p<0.001), as were hospitals in less competitive markets (OR=2.15 for hospitals in the most relative to least concentrated market quartile; p=0.04). ConclusionsDespite an uptick in hospital HIE participation since the start of HITECH, the majority of hospitals still do not engage in HIE and there is large state-to-state variation. Specific types of hospitals appear to feel that they are better off not engaging in HIE. ImplicationsStronger policies and incentives may be needed to convince organizations to share their data electronically. Pursuing these is critical to ensuring that the highly anticipated quality and efficiency gains from our large national investment in health information technology are realized.

Introduction and Commentary for Special Issue on Health Information Technology

In 2009, as part of the so-called Stimulus Act, the federal government made a large investment in health information technology (health IT), designating up to $30 billion in incentive payments to providers to adopt and “meaningfully use” certified electronic health record (EHR) systems. The EHR Incentive Program’s inclusion in this legislation—also known as the American Recovery and Reinvestment Act (ARRA)—was apt (111th Congress of the United States 2009). Not only could health IT funding stimulate economic activity but it could also help modernize the health care system, lay the foundation for new health care delivery and payment models, and promote a learning health care environment.

The effects of health information technology on the costs and quality of medical care

Information technology has been linked to productivity growth in a wide variety of sectors, and health information technology (HIT) is a leading example of an innovation with the potential to transform industry-wide productivity. This paper analyzes the impact of health information technology (HIT) on the quality and intensity of medical care. Using Medicare claims data from 1998 to 2005, I estimate the effects of early investment in HIT by exploiting variation in hospitals’ adoption statuses over time, analyzing 2.5 million inpatient admissions across 3900 hospitals. HIT is associated with a 1.3% increase in billed charges (p-value: 5.6%), and there is no evidence of cost savings even five years after adoption. Additionally, HIT adoption appears to have little impact on the quality of care, measured by patient mortality, adverse drug events, and readmission rates.

Does Clinical Decision Support Reduce Unwarranted Variation in Yield of CT Pulmonary Angiogram?

ObjectiveThe study objective was to determine whether previously documented effects of clinical decision support on computed tomography for pulmonary embolism in the emergency department (ie, decreased use and increased yield) are due to a decrease in unwarranted variation. We evaluated clinical decision support effect on intra- and inter-physician variability in the yield of pulmonary embolism computed tomography (PE-CT) in this setting. MethodsThe study was performed in an academic adult medical center emergency department with 60,000 annual visits. We enrolled all patients who had PE-CT performed 18 months pre- and post-clinical decision support implementation. Intra- and inter-physician variability in yield (% PE-CT positive for acute pulmonary embolism) were assessed. Yield variability was measured using logistic regression accounting for patient characteristics. ResultsA total of 1542 PE-CT scans were performed before clinical decision support, and 1349 PE-CT scans were performed after clinical decision support. Use of PE-CT decreased from 26.5 to 24.3 computed tomography scans/1000 patient visits after clinical decision support (P < .02); yield increased from 9.2% to 12.6% (P < .01). Crude inter-physician variability in yield ranged from 2.6% to 20.5% before clinical decision support and from 0% to 38.1% after clinical decision support. After controlling for patient characteristics, the post-clinical decision support period showed significant inter-physician variability (P < .04). Intra-physician variability was significant in 3 of the 25 physicians (P < .04), all with increased yield post-clinical decision support. ConclusionsOverall PE-CT yield increased after clinical decision support implementation despite significant heterogeneity among physicians. Increased inter-physician variability in yield after clinical decision support was not explained by patient characteristics alone and may be due to variable physician acceptance of clinical decision support. Clinical decision support alone is unlikely to eliminate unwarranted variability, and additional strategies and interventions may be needed to help optimize acceptance of clinical decision support to maximize returns on national investments in health information technology.

President's column: interoperability--the 30% solution: from dialog and rhetoric to reality

The purpose of the Messages from AMIA section is to provide a forum for AMIA to inform and involve its current and potential members about the goals and the directions of the association. These messages, which reflect the directions and opinions of AMIA leaders only, are intended to inspire members and readers to connect with the association on strategic objectives and activities. See also www.amia.org/presidents-page. Over the last decade, the members of AMIA have been at the forefront of calls for interoperability. But, our advocacy has been a call for interoperability, not a demand . The time has come for healthcare leaders to move from passive dialog and rhetorical support of interoperability toward active engagement with the public, government, and private industry to create the reality of interoperability. Clearly, advantages and benefits would accrue to society, our children, our parents, and ourselves if we were to finally see the full realization of our investment in health information technology by fostering interoperability. The statistics are stark. Fully 20% of preventable medical errors in the USA are caused by the lack of immediate access to health information according to the US Department of Health and Human Services.1 In addition, one of every seven primary care visits is compromised because of missing medical information.2 Furthermore, an independent review of physician treatment plans suggests that decision-making could be improved for 14% of admissions, where many of the benefits would have delayed manifestations.3 In yet another study, it is estimated that only 55% of adults in the USA receive the care recommended by the best protocols and guidelines.4 Finally, the main reason for …

Predicting the impact of hospital health information technology adoption on patient satisfaction

To develop and explore the predictability of patient perceptions of satisfaction through the hospital adoption of health information technology (HIT), leading to a better understanding of the benefits of increased HIT investment. The solution proposed is based on comparing the predictive capability of artificial neural networks (ANNs) with the adaptive neuro-fuzzy inference system (ANFIS). The latter integrates artificial neural networks and fuzzy logic and can handle certain complex problems that include fuzziness in human perception, and non-normal and non-linear data. Secondary data from two surveys were combined to develop the model. Hospital HIT adoption capability and use indicators in the Canadian province of Ontario were used as inputs, while patient satisfaction indicators of healthcare services in acute hospitals were used as outputs. Eight different types of models were trained and tested for each of four patient satisfaction dimensions. The accuracy of each predictive model was evaluated through statistical performance measures, including root mean square error (RMSE), and adjusted coefficient of determination R(2)(Adjusted). For all four patient satisfaction indicators, the performance of ANFIS was found to be more effective (R(Adjusted)(2)=0.99) when compared with the results of ANN modeling in predicting the impact of HIT adoption on patient satisfaction (R(Adjusted)(2)=0.86-0.88). The impact of HIT adoption on patient satisfaction was obtained for different HIT adoption scenarios using ANFIS simulations. The results through simulation scenarios revealed that full implementation of HIT in hospitals can lead to significant improvement in patient satisfaction. We conclude that the proposed ANFIS modeling technique can be used as a decision support mechanism to assist government and policy makers in predicting patient satisfaction resulting from the implementation of HIT in hospitals.

Continuity of care: important, but only the first step

Continuity of care is critical to achieving the best outcomes, especially for patients with chronic conditions. Israel’s strong commitment to primary care as a central organizing concept of the health system, accompanied by investments in health information technology and training primary care physicians, has contributed to its impressive levels of continuity of care. Taking the next steps toward a comprehensive system of patient- and population-centered care for proactive management of patients with chronic conditions has much potential to further enhance outcomes and reduce costs.