Robert S. Rudin

Health Information Technology: An Updated Systematic Review With a Focus on Meaningful Use

In the United States, adoption of health information technology (IT) has been accelerated by the meaningful use incentive program, which provides financial incentives to individual health care providers and organizations that demonstrate that they use certified health IT to meet a set of several use criteria specified by the Centers for Medicare & Medicaid Services (1, 2). This increase in use has been accompanied by a concomitant increase in the number of published evaluations of health IT. Because of the rapidly expanding evidence base, the Office of the National Coordinator requested a systematic update of the literature (3). The objective of this review is to update previous reviews (46) and examine recent evidence that relates health IT functionalities prescribed in meaningful use regulations to health care quality (including process, health, and patient and provider satisfaction outcomes), safety (including medication safety and other manifestations of patient safety), and efficiency (including costs, utilization, timeliness, and time burden of health care). Methods Although we did not develop a formal protocol for this update, we based it on the search strategy, inclusion and exclusion criteria, data collection, and synthesis methods from previous reviews on health IT (46). Data Sources and Search Strategy A 5-person technical expert panel, which included academic, health care delivery, and policy experts in health IT, guided the review process. Literature searches were based on the strategy initially used by Chaudhry and colleagues (4) and updated by Goldzweig (5) and Buntin (6) and their respective colleagues. This strategy uses broad-based search terms for the English-language literature indexed in PubMed. Our initial search covered the period of January 2010 to November 2011. We used a computer-aided screening method (7) to update that search to November 2012, and then updated searches again to August 2013 (Tables 1 and 2 of the Supplement). Our expert panel reviewed the search results and suggested additional articles that may have been missed. Supplement. Tables Study Selection Two expert reviewers used a Web-based system, DistillerSR (8), to independently select studies. Following the methods of our previous reviews, we considered hypothesis-testing studies of health IT effects and descriptive quantitative studies for inclusion. We classified articles as hypothesis-testing if the investigators compared data between groups or across periods and used statistical tests to assess differences. Hypothesis-testing studies were further classified by study design (such as randomized, controlled trials [RCTs]). To be included, a study needed to evaluate a health IT functionality encompassed by the meaningful use regulations. The meaningful use requirements specify 25 criteria for health IT functionality and use (such as Use CPOE for medication orders), of which providers must meet a portion to receive incentive payments (Tables 3 and 4 of the Supplement). Data Synthesis and Analysis Using a structured form, we abstracted information about the following: study design; clinical setting, health care conditions, and aspects of care assessed; research sites; health IT type (commercial or homegrown); meaningful use functionality evaluated; and context and implementation details. We adapted criteria developed to assess health IT applications in patient safety to classify articles according to reported context (4 domains) and implementation details (7 specific components) (9). We used a modified version of the outcome result classification framework (positive, mixed-positive, neutral, or negative), originally used by Buntin and colleagues (6), to assess outcomes. Our adaptations made the classification framework more conservative than the original framework, thus increasing the likelihood that an articles findings would be classified as mixed-positive, neutral, or negative (Table 5 of the Supplement). The functionality evaluated, context domains, implementation components, and article outcomes were classified by dual-review, and conflicts were resolved by consensus. Role of the Funding Source This project was done under contract to the Office of the National Coordinator. Representatives of the Office of the National Coordinator were briefed on study findings and reviewed a draft manuscript but were not involved in the analysis or decision to submit the manuscript for publication. Results We deemed 2482 of 12 678 titles identified in the searches potentially relevant. We excluded 2023 of these after abstract review and another 223 after full-text review. The 236 articles selected for review encompassed 278 outcomes because some articles addressed several aspects of care and outcomes (Figure). The full list of the included studies can be found in the reference list in the Supplement. Quality outcomes (n= 170) were evaluated more than safety (n= 46) and efficiency (n= 62) outcomes combined, and care processes (n= 103) outcomes were more than twice as common as health outcomes (n= 47). Studies with a simple prepost design were most common (31%), followed by RCTs (25%) and time-series studies (11%). More than one half (53%) evaluated commercial health IT products, although approximately one quarter did not report whether the products that were evaluated were commercial or homegrown. Figure. Evidence search and selection. Meaningful Use Functionalities Most studies addressed clinical decision support (CDS)(n= 85 [36%]); computerized provider order entry (CPOE) (n= 49 [21%]); or multifunctional health IT interventions (n= 47 [20%]), which evaluated broad IT interventions, such as electronic health records (EHRs), that encompassed many of the functionalities required under meaningful use. Twelve of the 25 functionalities in the meaningful use regulations, such as capacity to track vital signs or maintain medication allergy lists, were not specifically evaluated in any studies. These features, however, were likely critical to the functionality of IT interventions (such as CDS) that were evaluated in many studies. Nevertheless, for some aspects of meaningful use, such as implement systems to protect privacy and security, no eligible studies were found. Quality Outcomes Overall, 147 articles assessed the effect of health IT on 170 quality-related outcomes (Tables 6 and 7 of the Supplement). More than one half of these studies assessed CDS alerts and reminders; the most commonly studied outcomes were medication management, screening and preventive care, and process quality for diabetes and venous thromboembolism. More than three quarters of studies of alerts and reminders, including large quasi-experimental studies and RCTs, reported positive effects. Notable among these included a 1-year prospective, cluster randomized trial in 12 primary care pediatric practices that reported that EHR-embedded CDS substantially improved the use of asthma control medications, spirometry, and the maintenance of care plans (10); a study of nearly 20 000 surgical patients that found that CDS was associated with a 30% increase in adherence to infection prevention guidelines (11); a controlled before-and-after study compared 360 primary care physicians in New York, New York, with a matched set of control physicians and reported that EHR-sensitive measures of process quality (such as eye examinations and urine testing for patients with diabetes) improved substantially only in practices that received high levels of technical assistance in implementing their EHRs (12); and a before-and-after study showing that a CDS intervention in a commercial health IT system was associated with a significant increase in venous thromboembolism prophylaxis and a substantial decline in the rate of venous thromboembolism among nearly 40 000 patients admitted to a single academic medical center (13). Eighteen percent of studies reported no statistically significant improvements in quality or even negative effects. An illustrative example of these studies reported that on the basis of a nationally representative sample of physician office visits, EHRs were associated with poor depression care among patients diagnosed with multiple chronic conditions. The authors of this study hypothesized that EHR workflows gave precedence to the treatment of physical conditions, and therefore psychosocial problems were left unaddressed (14). Safety Outcomes Forty-six studies investigated the effects of health IT functionalities on patient safety outcomes, focusing exclusively on medication safety (Tables 8 and 9 of the Supplement). Approximately 78% of these studies reported at least some positive effects. Of note, benefits were found for a wide range of medication safety outcomes in various care settings. Automated dose calculation features within CPOE systems were found to have significant relative reductions in errors in medication dosage ranging from 37% to 80% (1517), and authors of 1 study reported that innovative features, such as incorporation of an order verification screen with a patient picture, was associated with complete elimination of incorrect patient orders (18). However, a few studies reported that, in some cases, health IT did not have the desired effect on medication safety, and alert fatigue and incongruent workflows were described as barriers to successful use of these systems. Efficiency Outcomes We identified 58 articles that assessed the effect of health IT on 62 efficiency-related outcomes (Tables 10 and 11 of the Supplement). Cost effects ranged from a 75% decrease to a 69% increase in the targeted costs. A few of the studies clustered in the range of 6% to 12% increases in the targeted costs. Understanding the relationship between health IT and health care utilization is complicated by external factors, such as the payment environment in which the health care providers operate. We identified some large studies that reported that patient and provider acces

Continuity and the Costs of Care for Chronic Disease

IMPORTANCE Better continuity of care is expected to improve patient outcomes and reduce health care costs, but patterns of use, costs, and clinical complications associated with the current patterns of care continuity have not been quantified. OBJECTIVE To measure the association between care continuity, costs, and rates of hospitalizations, emergency department visits, and complications for Medicare beneficiaries with chronic disease. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of insurance claims data for a 5% sample of Medicare beneficiaries experiencing a 12-month episode of care for congestive heart failure (CHF, n = 53,488), chronic obstructive pulmonary disease (COPD, n = 76,520), or type 2 diabetes mellitus (DM, n = 166,654) in 2008 and 2009. MAIN OUTCOMES AND MEASURES Hospitalizations, emergency department visits, complications, and costs of care associated with the Bice-Boxerman continuity of care (COC) index, a measure of the outpatient COC related to conditions of interest. RESULTS The mean (SD) COC index was 0.55 (0.31) for CHF, 0.60 (0.34) for COPD, and 0.50 (0.32) for DM. After multivariable adjustment, higher levels of continuity were associated with lower odds of inpatient hospitalization (odds ratios for a 0.1-unit increase in COC were 0.94 [95% CI, 0.93-0.95] for CHF, 0.95 [0.94-0.96] for COPD, and 0.95 [0.95-0.96] for DM), lower odds of emergency department visits (0.92 [0.91-0.92] for CHF, 0.93 [0.92-0.93] for COPD, and 0.94 [0.93-0.94] for DM), and lower odds of complications (odds ratio range, 0.92-0.96 across the 3 complication types and 3 conditions; all P < .001). For every 0.1-unit increase in the COC index, episode costs of care were 4.7% lower for CHF (95% CI, 4.4%-5.0%), 6.3% lower for COPD (6.0%-6.5%), and 5.1% lower for DM (5.0%-5.2%) in adjusted analyses. CONCLUSIONS AND RELEVANCE Modest differences in care continuity for Medicare beneficiaries are associated with sizable differences in costs, use, and complications.

Unraveling the IT Productivity Paradox — Lessons for Health Care

Debate is ongoing about the wisdom of the

Let the left hand know what the right is doing: a vision for care coordination and electronic health records

27 billion federal investment driving the adoption of health information technology (IT). Debate about ITs productivity yield echoes earlier debates from other sectors of the economy, which carry important lessons.

Accelerating Innovation in Health IT

Despite the potential for electronic health records to help providers coordinate care, the current marketplace has failed to provide adequate solutions. Using a simple framework, we describe a vision of information technology capabilities that could substantially improve four care coordination activities: identifying collaborators, contacting collaborators, collaborating, and monitoring. Collaborators can include any individual clinician, caregiver, or provider organization involved in care for a given patient. This vision can be used to guide the development of care coordination tools and help policymakers track and promote their adoption.

Measuring care continuity: A comparison of claims-based methods

Even as information technology transforms many industries, the pace of innovation in health IT continues to lag. One fundamental barrier that has not yet received due attention is the disconnect between health IT developers and users.

The Litmus Test for Health Information Exchange Success: Will Small Practices Participate?: Comment on “Health Information Exchange”

Background:Assessing care continuity is important in evaluating the impact of health care reform and changes to health care delivery. Multiple measures of care continuity have been developed for use with claims data. Objective:This study examined whether alternative continuity measures provide distinct assessments of coordination within predefined episodes of care. Research Design and Subjects:This was a retrospective cohort study using 2008–2009 claims files for a national 5% sample of beneficiaries with congestive heart failure, chronic obstructive pulmonary disease, and diabetes mellitus. Measures:Correlations among 4 measures of care continuity—the Bice-Boxerman Continuity of Care Index, Herfindahl Index, usual provider of care, and Sequential Continuity of Care Index—were derived at the provider- and practice-levels. Results:Across the 3 conditions, results on 4 claims-based care coordination measures were highly correlated at the provider-level (Pearson correlation coefficient r=0.87–0.98) and practice-level (r=0.75–0.98). Correlation of the results was also high for the same measures between the provider- and practice-levels (r=0.65–0.92). Conclusions:Claims-based care continuity measures are all highly correlated with one another within episodes of care.

The Clinical Pharmacy Specialist: Part of the Solution

The movement to establish widespread health information exchange (HIE) in the U.S. has thus far shown mixed results.[1] Although the number of sustainable HIEs is increasing, it appears that few of them so far encompass a large enough portion of healthcare providers and patients to significantly impact care delivery. Even long-standing and eminently successful HIEs have been slow to gather clinical data from independent office-based practices, [2] which account for the majority of healthcare utilization in the U.S.[3] Nor is much known about the extent to which existing HIEs organize clinical data as patient-centric, longitudinal records, accessible to all providers caring for a patient. Many HIEs achieve sustainability by automating the delivery of test results,[1] rather than by offering more robust data exchange functionality in which providers can actually view a wide array of clinical data generated by other providers, formatted as a unified record for each patient. Understanding why providers, especially those who are office-based, do or do not participate in HIE has become even more urgent now, to inform how ARRA’s payments for meaningful use of HIE can be effectively targeted. In this issue of the Archives, Fontaine et al explore HIE from the perspectives of small primary care practices in Minnesota.[4] Because small primary care practices provide a large portion of care in many regions, they represent critical stakeholders to HIEs, both as sources of clinical information for use by other providers and as consumers of other providers’ clinical data to inform and coordinate their patients’ care. These stakeholders, however, may be the hardest group to engage in HIEs because of their difficult financial situations and large patient loads.[5] And with the shortage of primary care physicians in the US, few will think to use HIE as a competitive weapon to attract new patients, as many already have full panels.[6][7] That small primary care practices are both essential for HIE success and difficult to engage makes their perspective an important one to examine. The authors’ findings reveal a diversity of information sharing activities among nine small primary care practices, selected for their close geographic proximity to HIE initiatives and varying degrees of clinical data sharing activities. The practices seem well integrated electronically with laboratories, an encouraging finding. Less encouraging is that only one in 9 are sharing data electronically with hospitals, and very troubling, not a single practice was found to be electronically sharing clinical data with another non-affiliated physician practice and “no practice was fully involved in a regional HIE and HIE was not part of most practices’ short-term strategic plans.”[4] If small primary care providers are not engaged in robust clinical information sharing in Minnesota, a state with a relatively high degree of health IT penetration, it can reasonably be assumed that few primary care providers, other than those in large integrated delivery systems, are exchanging clinical data with other providers electronically, and HIE has a long way to go to realize its potential value. [8] What is preventing small primary care providers from joining HIEs? What could motivate them to participate? Through extensive interviews, Fontaine et al begin to answer these questions and provide a window into the incentive structures in which these critical stakeholders operate. They describe a complex array of forces at work but perhaps the most potentially powerful one is absent: patients demanding that their records be shared electronically among their caregivers. Patients appear to not yet be aware that HIEs exist or of their potential to improve care and reduce costs.[8] It may be that patient demand for HIE will not grow until patients come to expect access to their own health data in a organized electronic form: when HIEs offer patient portals, allowing them to view and manipulate data from their providers and clearly see which providers are participating and which are not. If the intent of ARRA’s meaningful use payments is to stimulate HIE to the point of sustainability, the payments may need to advance HIE to the point where patients can access their own clinical data so that market forces – in the form of patients’ expectations – become strong enough to bring providers toward HIEs. However, in today’s healthcare provider market, patient demand for HIE may only motivate a small portion of primary care providers to take part in HIE. The shortage of primary care doctors suggests that few will need to compete for patients, and they will be especially reluctant to join an HIE if the membership fee further strains their finances. Patients’ expectations notwithstanding, the barriers described by Fontaine et al seem daunting. Among the most challenging is the lack of interoperability among vendors. One measure of success of the meaningful use payments will be how effectively they stimulate EHR vendors to compete based on interoperability and to leave behind business strategies aimed at locking doctors and patients in to proprietary systems. Fostering such competition should be one of the meaningful use payments’ central and explicit goals. Probably the most significant barrier to providers joining HIEs today is the lack of a business case. The meaningful use payments may ease this burden in the next few years, but the payments may not be large enough to be worthwhile for many practices, especially small primary care offices, and there is no mechanism established to pay for HIEs after the meaningful use payments run out. One possibly way to make the business case was identified as a motivating factor by Fontaine et al and is consistent with findings of my own research: quality measuring and reporting capabilities provided by HIEs.[9] Quality reporting appeared to be a “frequently mentioned motivation for establishing HIEs” in Minnesota.[4] If HIEs are adequately designed to support quality measurements, and those measurements are expanded to become more comprehensive, quality-based performance payments may have the potential pick up where the meaningful use payments leave off. For this to happen, however, the quality metrics must measure performance at a fitting level of analysis in an organization or network of providers, which may require grouping provider practices together. Fortunately recent momentum to establish accountable care organizations (ACOs) may supply the framework for such grouping. [10] ACOs and HIE will need to work together: ACOs will be interested in the services that HIEs offer for improved clinical decision-making and cost avoidance, and HIEs will look to ACOs to help them achieve sustainability. Another way to make the business case to small primary care physicians is through their participation in a patient-centered medical home program.[11] This approach may cover providers’ HIE membership by providing payments that “support coordination of care” between providers and “use of health information technology for quality improvement.” What will it take for small primary care practices to participate in HIEs? In the short-term, the meaningful use payments may motivate many, but some providers may still need more direct subsidies or be allowed to contribute clinical data to HIEs for free and only pay for access to collected clinical data. In the long-term, HIE must be embedded in the larger healthcare system’s incentive structures. When the meaningful use payments expire, if patient demand for HIE, quality-based performance payments, ACOs, and patient-based medical home projects have not gained in influence, HIEs may still suffer from the difficulties of sustainability and attracting small providers that they grapple with today, and much of the potential benefit of HIE will not be realized.

Understanding the decisions and values of stakeholders in health information exchanges: experiences from Massachusetts.

RAND Corporation, Boston, MA, USA; Department of Medicine, Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA; Center for Healthcare Organization and Implementation Research, Bedford VA Medical Center, Bedford, MA, USA; Department of Health Policy and Management, Boston University School of Public Health, Boston, MA, USA; Department of Pharmacy Practice and Science, College of Pharmacy, University of Iowa, IowaCity, IA, USA; Department of FamilyMedicine, CollegeofMedicine, University of Iowa, Iowa City, IA, USA.