Elizabeth A Roth

Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care

Key Summary Points Health information technology has been shown to improve quality by increasing adherence to guidelines, enhancing disease surveillance, and decreasing medication errors. Much of the evidence on quality improvement relates to primary and secondary preventive care. The major efficiency benefit has been decreased utilization of care. Effect on time utilization is mixed. Empirically measured cost data are limited and inconclusive. Most of the high-quality literature regarding multifunctional health information technology systems comes from 4 benchmark research institutions. Little evidence is available on the effect of multifunctional commercially developed systems. Little evidence is available on interoperability and consumer health information technology. A major limitation of the literature is its generalizability. Health care experts, policymakers, payers, and consumers consider health information technologies, such as electronic health records and computerized provider order entry, to be critical to transforming the health care industry (1-7). Information management is fundamental to health care delivery (8). Given the fragmented nature of health care, the large volume of transactions in the system, the need to integrate new scientific evidence into practice, and other complex information management activities, the limitations of paper-based information management are intuitively apparent. While the benefits of health information technology are clear in theory, adapting new information systems to health care has proven difficult and rates of use have been limited (9-11). Most information technology applications have centered on administrative and financial transactions rather than on delivering clinical care (12). The Agency for Healthcare Research and Quality asked us to systematically review evidence on the costs and benefits associated with use of health information technology and to identify gaps in the literature in order to provide organizations, policymakers, clinicians, and consumers an understanding of the effect of health information technology on clinical care (see evidence report at www.ahrq.gov). From among the many possible benefits and costs of implementing health information technology, we focus here on 3 important domains: the effects of health information technology on quality, efficiency, and costs. Methods Analytic Frameworks We used expert opinion and literature review to develop analytic frameworks (Table) that describe the components involved with implementing health information technology, types of health information technology systems, and the functional capabilities of a comprehensive health information technology system (13). We modified a framework for clinical benefits from the Institute of Medicines 6 aims for care (2) and developed a framework for costs using expert consensus that included measures such as initial costs, ongoing operational and maintenance costs, fraction of health information technology penetration, and productivity gains. Financial benefits were divided into monetized benefits (that is, benefits expressed in dollar terms) and nonmonetized benefits (that is, benefits that could not be directly expressed in dollar terms but could be assigned dollar values). Table. Health Information Technology Frameworks Data Sources and Search Strategy We performed 2 searches (in November 2003 and January 2004) of the English-language literature indexed in MEDLINE (1995 to January 2004) using a broad set of terms to maximize sensitivity. (See the full list of search terms and sequence of queries in the full evidence report at www.ahrq.gov.) We also searched the Cochrane Central Register of Controlled Trials, the Cochrane Database of Abstracts of Reviews of Effects, and the Periodical Abstracts Database; hand-searched personal libraries kept by content experts and project staff; and mined bibliographies of articles and systematic reviews for citations. We asked content experts to identify unpublished literature. Finally, we asked content experts and peer reviewers to identify newly published articles up to April 2005. Study Selection and Classification Two reviewers independently selected for detailed review the following types of articles that addressed the workings or implementation of a health technology system: systematic reviews, including meta-analyses; descriptive qualitative reports that focused on exploration of barriers; and quantitative reports. We classified quantitative reports as hypothesis-testing if the investigators compared data between groups or across time periods and used statistical tests to assess differences. We further categorized hypothesis-testing studies (for example, randomized and nonrandomized, controlled trials, controlled before-and-after studies) according to whether a concurrent comparison group was used. Hypothesis-testing studies without a concurrent comparison group included those using simple prepost, time-series, and historical control designs. Remaining hypothesis-testing studies were classified as cross-sectional designs and other. We classified quantitative reports as a predictive analysis if they used methods such as statistical modeling or expert panel estimates to predict what might happen with implementation of health information technology rather than what has happened. These studies typically used hybrid methodsfrequently mixing primary data collection with secondary data collection plus expert opinion and assumptionsto make quantitative estimates for data that had otherwise not been empirically measured. Cost-effectiveness and cost-benefit studies generally fell into this group. Data Extraction and Synthesis Two reviewers independently appraised and extracted details of selected articles using standardized abstraction forms and resolved discrepancies by consensus. We then used narrative synthesis methods to integrate findings into descriptive summaries. Each institution that accounted for more than 5% of the total sample of 257 papers was designated as a benchmark research leader. We grouped syntheses by institution and by whether the systems were commercially or internally developed. Role of the Funding Sources This work was produced under Agency for Healthcare Research and Quality contract no. 2002. In addition to the Agency for Healthcare Research and Quality, this work was also funded by the Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services, and the Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services. The funding sources had no role in the design, analysis, or interpretation of the study or in the decision to submit the manuscript for publication. Data Synthesis Literature Selection Overview Of 867 articles, we rejected 141 during initial screening: 124 for not having health information technology as the subject, 4 for not reporting relevant outcomes, and 13 for miscellaneous reasons (categories not mutually exclusive). Of the remaining 726 articles, we excluded 469 descriptive reports that did not examine barriers (Figure). We recorded details of and summarized each of the 257 articles that we did include in an interactive database (healthit.ahrq.gov/tools/rand) that serves as the evidence table for our report (14). Twenty-four percent of all studies came from the following 4 benchmark institutions: 1) the Regenstrief Institute, 2) Brigham and Womens Hospital/Partners Health Care, 3) the Department of Veterans Affairs, and 4) LDS Hospital/Intermountain Health Care. Figure. Search flow for health information technology ( HIT ) literature. Pediatrics Types and Functions of Technology Systems The reports addressed the following types of primary systems: decision support aimed at providers (63%), electronic health records (37%), and computerized provider order entry (13%). Specific functional capabilities of systems that were described in reports included electronic documentation (31%), order entry (22%), results management (19%), and administrative capabilities (18%). Only 8% of the described systems had specific consumer health capabilities, and only 1% had capabilities that allowed systems from different facilities to connect with each other and share data interoperably. Most studies (n= 125) assessed the effect of the systems in the outpatient setting. Of the 213 hypothesis-testing studies, 84 contained some data on costs. Several studies assessed interventions with limited functionality, such as stand-alone decision support systems (15-17). Such studies provide limited information about issues that todays decision makers face when selecting and implementing health information technology. Thus, we preferentially highlight in the following paragraphs studies that were conducted in the United States, that had empirically measured data on multifunctional systems, and that included health information and data storage in the form of electronic documentation or order-entry capabilities. Predictive analyses were excluded. Seventy-six studies met these criteria: 54 from the 4 benchmark leaders and 22 from other institutions. Data from Benchmark Institutions The health information technology systems evaluated by the benchmark leaders shared many characteristics. All the systems were multifunctional and included decision support, all were internally developed by research experts at the respective academic institutions, and all had capabilities added incrementally over several years. Furthermore, most reported studies of these systems used research designs with high internal validity (for example, randomized, controlled trials). Appendix Table 1 (18-71) provides a structured summary of each study from the 4 benchmark institutions. This table also includes studies that met inclusion criteria not highlighted in this synthesis (26, 27, 30, 39, 40, 53, 62, 65, 70, 71). The data supported 5 primary themes (3 directly r

Interventions for the prevention of falls in older adults: systematic review and meta-analysis of randomised clinical trials

Abstract Objective To assess the relative effectiveness of interventions to prevent falls in older adults to either a usual care group or control group. Table 2 Components of multifactorial falls risk assessment Trial Orthostatic blood pressure Vision Balance and gait Drug review Instrumental activities of daily living or activities of daily living Cognitive evaluation Environmental hazards Other Carpenter 1990w4 No No No No Yes No No Fabacher 1994w13 Yes Yes Yes Yes Yes Yes Yes Assessment of hearing and depression Rubenstein 1990w30 Yes Yes Yes Yes Yes Yes Yes Neurological and musculoskeletal examination, laboratory tests, 24 hour heart monitor Tinetti 1994w37 Yes No Yes Yes No No Yes Muscle strength and range of motion Wagner 1994w39 No Yes No Yes No No Yes Hearing, assessment of alcohol misuse, assessment of physical activity Gallagher 1996w15 Yes Yes Yes Yes Yes Yes Yes List of health problems Coleman 1999w7 No No No Yes No No No Self management skills, health assessment Close 1999w6 Yes Yes Yes Yes Yes Yes Yes Affect, carotid sinus studies (if clinical suspicion) McMurdo 2000w21 Yes Yes No Yes No No No Review of lighting in environment Van Haastregt 2000w38 No No No Yes Yes Yes Yes Physical health, psychosocial functioning Millar 1999w24 Yes Yes No Yes No No No Review of lighting in environment Crome 2000w8* Jensen 2002w17 No Yes Yes Yes Yes Yes Yes Hearing, review of lighting in environment, assistive device (for example, cane, walker), review of use of device, and repair of device if needed See table A on bmj.com for details of references. * No specific components stated. Design Systematic review and meta-analyses. Data sources Medline, HealthSTAR, Embase, the Cochrane Library, other health related databases, and the reference lists from review articles and systematic reviews. Data extraction Components of falls intervention: multifactorial falls risk assessment with management programme, exercise, environmental modifications, or education. Results 40 trials were identified. A random effects analysis combining trials with risk ratio data showed a reduction in the risk of falling (risk ratio 0.88, 95% confidence interval 0.82 to 0.95), whereas combining trials with incidence rate data showed a reduction in the monthly rate of falling (incidence rate ratio 0.80, 0.72 to 0.88). The effect of individual components was assessed by meta-regression. A multifactorial falls risk assessment and management programme was the most effective component on risk of falling (0.82, 0.72 to 0.94, number needed to treat 11) and monthly fall rate (0.63, 0.49 to 0.83; 11.8 fewer falls in treatment group per 100 patients per month). Exercise interventions also had a beneficial effect on the risk of falling (0.86, 0.75 to 0.99, number needed to treat 16) and monthly fall rate (0.86, 0.73 to 1.01;2.7). Conclusions Interventions to prevent falls in older adults are effective in reducing both the risk of falling and the monthly rate of falling. The most effective intervention was a multifactorial falls risk assessment and management programme. Exercise programmes were also effective in reducing the risk of falling.

How Useful Are Unpublished Data from the Food and Drug Administration in Meta-Analysis?

The goals of this systematic review and meta-analysis were to ascertain whether studies of nonsteroidal anti-inflammatory drugs (NSAIDs) summarized in the FDA reviews are ultimately published, to compare the methodologic and population characteristics of studies summarized in the FDA reviews with those reported in peer reviewed literature, and to compare the pooled relative risk of dyspepsia from NSAIDs in each data source. Summary measures of risk difference were calculated with a random effects model; meta-regression was used to assess the effect of study covariates. Among 37 studies described in the FDA reviews, one was published. Sample size, gender distribution, indication for drug use, and methodologic quality did not vary significantly between the published and FDA data. The pooled risk ratio for dyspepsia obtained using published data (1.21) or FDA data (1.07) did not differ significantly or practically. Data from FDA reviews may be a viable data source for systematic reviews and meta-analyses but only after being subjected to the same methodologic scrutiny as published data.

Do NSAIDs cause dyspepsia|[quest]| a meta-analysis evaluating alternative dyspepsia definitions

OBJECTIVE:Nonsteroidal anti-inflammatory drugs (NSAIDs)may be associated with dyspepsia, but the relationship is obscured by variations in the terminology used to report GI symptoms. Using alternative definitions, we assessed the relationship between NSAID use and dyspepsia.METHODS:We searched MEDLINE, EMBASE, HEALTHSTAR, and BIOSIS databases (1966–1997) and New Drug Application reviews, identifying randomized, placebo-controlled trials (5 days or more duration) of any NSAID, reporting original data on GI complications. Based upon reported terms describing upper GI symptoms, we derived three definitions: strict, using terms synonymous with epigastric pain/discomfort; loose, (containing the strict definition plus terms for heartburn, nausea, bloating, anorexia, and vomiting); and a loose definition without heartburn terms (the loose-less-heartburn definition). Using each definition, we performed a random-effects model meta-analysis of the relationship between NSAID exposure and dyspepsia.RESULTS:Fifty-five published and 37 unpublished controlled NSAID trials met our inclusion criteria. The mean duration of the trials was 33.2 days (SD 40 days). Application of the strict definition resulted in a pooled risk ratio of dyspepsia for NSAIDs compared with placebo of 1.36 (95% CI = 1.11–1.67). For the loose definition, the pooled risk ratio was 1.13 (95% CI = 0.98–1.32). The loose-less-heartburn definition yielded a pooled risk ratio of 1.19 (95% CI = 1.03–1.39). In the placebo-treated control groups, the percent of patients reporting dyspepsia ranged from 2.3% (strict definition) to 4.2% (loose and loose-less-heartburn definitions).CONCLUSIONS:Using the strict definition, based solely on epigastric pain-related symptoms, NSAIDs increased the risk of dyspepsia by 36% (p < 0.05). These findings may be useful in creating a standardized definition of NSAID-related dyspepsia.