We studied all ED encounters over a 13-month period in which HIE data were accessed in all major emergency departments Memphis, Tennessee. HIE access encounter records were matched with similar encounter records without HIE access. Outcomes studied were ED-originated hospital admissions, admissions for observation, laboratory testing, head CT, body CT, ankle radiographs, chest radiographs, and echocardiograms. Our estimates employed generalized estimating equations for logistic regression models adjusted for admission type, length of stay, and Charlson co-morbidity index. Marginal probabilities were used to calculate changes in outcome variables and their financial consequences.

HIE data were accessed in approximately 6.8% of ED visits across 12 EDs studied. In 11 EDs directly accessing HIE data only through a secure Web browser, access was associated with a decrease in hospital admissions (adjusted odds ratio (OR)=0.27; p<0001). In a 12th ED relying more on print summaries, HIE access was associated with a decrease in hospital admissions (OR=0.48; p<0001) and statistically significant decreases in head CT use, body CT use, and laboratory test ordering.

Applied only to the study population, HIE access was associated with an annual cost savings of $1.9 million. Net of annual operating costs, HIE access reduced overall costs by $1.07 million. Hospital admission reductions accounted for 97.6% of total cost reductions.

Access to additional clinical data through HIE in emergency department settings is associated with net societal saving.

We conducted a retrospective study of 5166 adults with heart failure in three metropolitan EDs. Patients were termed internal if prior information was in the EHR upon ED presentation, otherwise external. Associations of internality with hospitalization, mortality, length of stay (LOS), and numbers of tests, procedures, and medications ordered in the ED were examined after adjusting for age, gender, race, marital status, comorbidities and hospitalization as a proxy for acuity level where appropriate.

At two EDs internals had lower odds of mortality if hospitalized (OR 0.55; 95% CI 0.38 to 0.81 and 0.45; 0.21 to 0.96), fewer laboratory tests during the ED visit (–4.6%; –8.9% to –0.1% and –14.0%; –19.5% to –8.1%) as well as fewer medications (–33.6%; –38.4% to –28.4% and –21.3%; –33.2% to –7.3%). At one of these two EDs, internals had lower odds of hospitalization (0.37; 0.22 to 0.60). At the third ED, internal patients only experienced a prolonged ED LOS (32.3%; 6.3% to 64.8%) but no other differences. There was no association with hospital LOS or number of procedures ordered.

EHR availability was associated with salutary outcomes in two of three ED settings and prolongation of ED LOS at a third, but evidence was mixed and causality remains to be determined.

An EHR may have the potential to be a valuable adjunct in the care of heart failure patients.

To estimate the long-term cost-effectiveness of a CDSS linked to evidence-based treatment recommendations for type 2 diabetes.

Using the Ontario Diabetes Economic Model, changes in factors (eg, HbA1c) from a randomized controlled trial were used to estimate cost-effectiveness. The cost of implementation, development, and maintenance of the core dataset, and projected diabetes-related complications were included. The base case assumed a 1-year treatment effect, 5% discount rate, and 40-year time horizon. Univariate, one-way sensitivity analyses were carried out by altering different parameter values. The perspective was the Ontario Ministry of Health and costs were in 2010 Canadian dollars.

The cost of implementing the intervention was $483 699. The one-year intervention reduced HbA1c by 0.2 and systolic blood pressure by 3.95 mm Hg, but increased body mass index by 0.02 kg/m², resulting in a relative risk reduction of 14% in the occurrence of amputation. The model estimated that the intervention resulted in an additional 0.0117 quality-adjusted life year; the incremental cost-effectiveness ratio was $160 845 per quality-adjusted life-year.

The web-based prototype decision support system slightly improved short-term risk factors. The model predicted moderate improvements in long-term health outcomes. This disease management program will need to develop considerable efficiencies in terms of costs and processes or improved effectiveness to be considered a cost-effective intervention for treating patients with type 2 diabetes.

Through literature review and iterative testing, metrics were developed that describe successes, justifiable overrides, provider non-adherence, and unintended adverse consequences of clinical decision support alerts. The framework was validated by applying it to a medication alerting system for patients with acute kidney injury (AKI).

Through expert review, the framework assesses each alert episode for appropriateness of the alert display and the necessity and urgency of a clinical response. Primary outcomes of the framework include the false positive alert rate, alert override rate, provider non-adherence rate, and rate of provider response appropriateness. Application of the framework to evaluate an existing AKI medication alerting system provided a more complete understanding of the process outcomes measured in the AKI medication alerting system. The authors confirmed that previous alerts and provider responses were most often appropriate.

The new evaluation model offers a potentially effective method for assessing the clinical appropriateness of synchronous interruptive medication alerts prior to evaluating patient outcomes in a comparative trial. More work can determine the generalizability of the framework for use in other settings and other alert types.

Qualitative analysis of 114 telephone interviews conducted with representatives from 97 organizations between February and September 2010, including 24 physician practices, 48 community pharmacies, and three mail-order pharmacies actively transmitting or receiving e-prescriptions via Surescripts.

Practices and pharmacies generally were satisfied with electronic transmission of new prescriptions but reported that the electronic renewal process was used inconsistently, resulting in inefficient workarounds for both parties. Practice communications with mail-order pharmacies were less likely to be electronic than with community pharmacies because of underlying transmission network and computer system limitations. While e-prescribing reduced manual prescription entry, pharmacy staff frequently had to complete or edit certain fields, particularly drug name and patient instructions.

Electronic transmission of new prescriptions has matured. Changes in technical standards and system design and more targeted physician and pharmacy training may be needed to address barriers to e-renewals, mail-order pharmacy connectivity, and pharmacy processing of e-prescriptions.

A retrospective cross-sectional analysis of data from three sources: CPOE/eMAR usage from HIMSS Analytics (2010), medication quality scores from CMS Hospital Compare (2010), and hospital characteristics from CMS Acute Inpatient Prospective Payment System (2009). The analysis focused on 11 quality indicators (January–December 2009) at 2603 medium-to-large (≥100 beds), non-federal acute-care hospitals measuring proportion of eligible patients given (or prescribed) recommended medications for conditions, including acute myocardial infarction, heart failure, and pneumonia, and surgical care improvement. Using technology adoption by 2008 as reference, hospitals were coded: (1) eMAR-only adopters (n=986); (2) CPOE-only adopters (n=115); and (3) adopters of both technologies (n=804); with non-adopters of both technologies as reference group (n=698). Hospitals were also coded for duration of use in 2-year increments since technology adoption. Hospital characteristics, historical measure-specific patient volume, and propensity scores for technology adoption were used to control for confounding factors. The analysis was performed using a generalized linear model (logit link and binomial family).

Relative to non-adopters of both eMAR and CPOE, the odds of adherence to all measures (except one) were higher by 14–29% for eMAR-only hospitals and by 13–38% for hospitals with both technologies, translating to a marginal increase of 0.4–2.0 percentage points. Further, each additional 2 years of technology use was associated with 6–15% higher odds of compliance on all medication measures for eMAR-only hospitals and users of both technologies.

Implementation and duration of use of health information technologies are associated with improved adherence to medication guidelines at US hospitals. The benefits are evident for adoption of eMAR systems alone and in combination with CPOE.

Mobile technologies provide a platform for electronic patient-centered medication management. MyMediHealth (MMH) is a medication management system that includes a medication scheduler, a medication administration record, and a reminder engine that sends text messages to cell phones. The object of this work was to extend MMH to allow two-way interaction using mobile phone-based SMS technology. Unprompted text-message communication with patients using natural language could engage patients in their healthcare, but presents unique natural language processing challenges. The authors developed a new functional component of MMH, the Patient-centered Automated SMS Tagging Engine (PASTE). The PASTE web service uses natural language processing methods, custom lexicons, and existing knowledge sources to extract and tag medication information from patient text messages.

A pilot evaluation of PASTE was completed using 130 medication messages anonymously submitted by 16 volunteers via a website. System output was compared with manually tagged messages.

Verified medication names, medication terms, and action terms reached high F-measures of 91.3%, 94.7%, and 90.4%, respectively. The overall medication name F-measure was 79.8%, and the medication action term F-measure was 90%.

Other studies have demonstrated systems that successfully extract medication information from clinical documents using semantic tagging, regular expression-based approaches, or a combination of both approaches. This evaluation demonstrates the feasibility of extracting medication information from patient-generated medication messages.

Prescribers of controlled substances affiliated with a regional health system were surveyed regarding current electronic prescribing (e-prescribing) activities, current prescribing of controlled substances, and expectations and barriers to the adoption of EPCS.

246 prescribers (response rate of 64%) represented a range of medical specialties, with 43.1% of these prescribers current users of e-prescribing for non-controlled substances. Reported issues with controlled substances included errors, pharmacy call-backs, and diversion; most prescribers expected EPCS to address many of these problems, specifically reduce medical errors, improve work flow and efficiency of practice, help identify prescription diversion or misuse, and improve patient treatment management. Prescribers expected, however, that it would be disruptive to practice, and over one-third of respondents reported that carrying a security authentication token at all times would be so burdensome as to discourage adoption.

Although adoption of e-prescribing has been shown to dramatically reduce medication errors, challenges to efficient processes and errors still persist from the perspective of the prescriber, that may interfere with the adoption of EPCS. Most prescribers regarded EPCS security measures as a small or moderate inconvenience (other than carrying a security token), with advantages outweighing the burden.

Prescribers are optimistic about the potential for EPCS to improve practice, but view certain security measures as a burden and potential barrier.

Qualitative study of six practices operating within the same multi-specialty ambulatory care organization using the same EHR system. Semistructured interviews and direct observation were conducted with all physicians, nurses, medical assistants, practice managers, and non-clinical staff from each practice.

An existing model of practice relationships was used to analyze communication patterns within the practices. Practice-level EHR use was defined and analyzed as the ways in which a practice uses an EHR as a collective or a group—including the degree of feature use, level of EHR-enabled communication, and frequency that EHR use changes in a practice. Interview and observation data were analyzed for themes. Based on these themes, within-practice communication patterns were categorized as fragmented or cohesive, and practice-level EHR use patterns were categorized as heterogeneous or homogeneous. Practices where EHR use was uniformly high across all users were further categorized as having standardized EHR use. Communication patterns and EHR use patterns were compared across the six practices.

Within-practice communication patterns were associated with practice-level EHR use patterns. In practices where communication patterns were fragmented, EHR use was heterogeneous. In practices where communication patterns were cohesive, EHR use was homogeneous. Additional analysis revealed that practices that had achieved standardized EHR use (uniformly high EHR use across all users) exhibited high levels of mindfulness and respectful interaction, whereas practices that were furthest from achieving standardized EHR use exhibited low levels of mindfulness and respectful interaction.

Within-practice communication patterns provide a unique perspective for exploring the issue of standardization in EHR use. A major fallacy of setting homogeneous EHR use as the goal for practice-level EHR use is that practices with uniformly low EHR use could be considered successful. Achieving uniformly high EHR use across all users in a practice is more consistent with the goals of current EHR adoption and use efforts. It was found that some communication patterns among practice members may enable more standardized EHR use than others. Understanding the linkage between communication patterns and EHR use can inform understanding of the human element in EHR use and may provide key lessons for the implementation of EHRs and other health information technologies.

The ethnographic qualitative study was conducted over a 9-month period in six emergency departments (ED) and eight ambulatory clinics in Memphis, Tennessee, USA. Data were collected using direct observation, informal interviews during observation, and formal semi-structured interviews. The authors observed for over 180 h, during which providers used the exchange 130 times.

HIE-related workflow was modeled for each ED site and ambulatory clinic group and substantial site-to-site workflow differences were identified. Common patterns in HIE-related workflow were also identified across all sites, leading to the development of two role-based workflow models: nurse based and physician based. The workflow elements framework was applied to the two role-based patterns. An in-depth description was developed of how providers integrated HIE into existing clinical workflow, including prompts for HIE use.

Workflow differed substantially among sites, but two general role-based HIE usage models were identified. Although providers used HIE to improve continuity of patient care, patient–provider trust played a significant role. Types of information retrieved related to roles, with nurses seeking to retrieve recent hospitalization data and more open-ended usage by nurse practitioners and physicians. User and role-specific customization to accommodate differences in workflow and information needs may increase the adoption and use of HIE.

Understanding end users' perspectives towards HIE technology is crucial to the long-term success of HIE. By applying qualitative methods, an in-depth understanding of HIE usage was developed.

Pre- and post-transition survey administered at six academic-affiliated ambulatory care practices from 2006 to 2008. Four practices transitioned to one commercial EHR and two practices to another. We compared respondents' expectations of, and satisfaction with, the newer EHR.

523 subjects were eligible: 217 were available before transition and 306 after transition. 162 pre-transition and 197 post-transition responses were received, yielding 75% and 64% response rates, respectively. Practitioners were more satisfied with the newer EHRs (64%) compared with the older (56%) (p=0.15) and a small majority (58%) were satisfied with the transition. Practitioners' satisfaction with the older EHRs for completing clinical tasks was high. The newer EHRs exceeded practitioner expectations regarding remote access (61% vs 74%; p=0.03). However, the newer EHRs did not meet practitioners' expectations regarding their ability to perform clinical tasks, or more globally, improve medication safety (81% vs 61%; p<0.001), efficiency (70% vs 44%; p<0.001), and quality of care (77% vs 67%; p=0.04).

Most practitioners had favorable opinions about EHRs and reported overall improved satisfaction with the newer EHRs. However, practitioners' high expectations of the newer EHRs were often unmet regarding facilitation of specific clinical tasks or for improving quality, safety, and efficiency.

To ensure practitioners' expectations, for instance regarding improvements in medication safety, are met, vendors should develop and implement refinements in their software as practices upgrade to newer, certified EHRs.

Eighty-one participants aged 50–76 years worked through the on-line PDS tool and completed questionnaires on computer self-efficacy, computer anxiety, attitudes to and beliefs about CRC screening before and after exposure to the PDS, and perceived usability and acceptability of the tool.

Repeated measures ANOVA found that PDS exposure led to a significant increase in knowledge about CRC and screening, and more positive attitudes to CRC screening as measured by factors from the Preventive Health Model. Perceived usability and acceptability of the PDS mediated changes in attitudes toward CRC screening (but not CRC knowledge), and computer self-efficacy and computer anxiety were significant predictors of individuals' perceptions of the tool.

Interventions designed to decrease computer anxiety, such as computer courses and internet training, may improve the acceptability of new health information technologies including internet-based decision support tools, increasing their impact on behavior change.

Peer-reviewed electronic databases and gray literature were searched to identify studies on HIT used to assist in the medication management process. Articles including an economic component were reviewed for further screening. For this review, full cost-effectiveness analyses, cost-utility analyses and cost-benefit analyses, as well as cost analyses, were eligible for inclusion and synthesis.

The 31 studies included were heterogeneous with respect to the HIT evaluated, setting, and economic methods used. Thus the data could not be synthesized, and a narrative review was conducted. Most studies evaluated computer decision support systems in hospital settings in the USA, and only five of the studied performed full economic evaluations.

Most studies merely provided cost data; however, useful economic data involves far more input. A full economic evaluation includes a full enumeration of the costs, synthesized with the outcomes of the intervention.

The quality of the economic literature in this area is poor. A few studies found that HIT may offer cost advantages despite their increased acquisition costs. However, given the uncertainty that surrounds the costs and outcomes data, and limited study designs, it is difficult to reach any definitive conclusion as to whether the additional costs and benefits represent value for money. Sophisticated concurrent prospective economic evaluations need to be conducted to address whether HIT interventions in the medication management process are cost-effective.

Qualitative analysis of interviews with identified key stakeholders followed by an electronic survey.

We conducted semi-structured interviews with 29 key Canadian HIT policy and opinion leaders and used a grounded theory approach to analyze the results. The informant sample was chosen to provide views from different stakeholder groups including national representatives and regional representatives from three Canadian provinces.

Canadian informants believed that much of the current US direction is positive, especially regarding incentives and meaningful use, but that there are key opportunities for the US to emphasize direct engagement with providers, define a clear business case for them, sponsor large scale evaluations to assess HIT impact in a broad array of settings, determine standards but also enable access to resources needed for mid-course corrections of standards when issues are identified, and, finally, leverage implementation of digital imaging systems.

Not all stakeholder groups were included, such as providers or patients. In addition, as in all qualitative research, a selection bias could be present due to the relatively small sample size.

Based on Canadian experience with HIT policy, stakeholders identified as lessons for the US the need to increase direct engagement with providers and the importance of defining the business case for HIT, which can be achieved through large scale evaluations, and of recognizing and leveraging successes as they emerge.

A diagnostic test assessment framework was used. A reference set of 2255 articles was created by hand-searching 22 biomedical journals in Medline, and each article was labeled as ‘relevant’, ‘not relevant’, or ‘possibly relevant’ for geriatric medicine. From the relevant articles, search terms were identified to compile different search strategies. The articles retrieved by the various search strategies were compared with articles from the reference set as the index test to create the search filters.

Sensitivity, specificity, precision, accuracy, and number-needed-to-read (NNR) were calculated by comparing the results retrieved by the different search strategies with the reference set.

The most sensitive search filter had a sensitivity of 94.8%, a specificity of 88.7%, a precision of 73.0%, and an accuracy of 90.2%. It had an NNR of 1.37. The most specific search filter had a specificity of 96.6%, a sensitivity of 69.1%, a precision of 86.6%, and an accuracy of 89.9%. It had an NNR of 1.15.

These geriatric search filters simplify searching for relevant literature and therefore contribute to better evidence-based practice. The filters are useful to both the clinician who wants to find a quick answer to a clinical question and the researcher who wants to find as many relevant articles as possible without retrieving too many irrelevant articles.

The authors used 394 days of query log (August 1, 2009 to August 29, 2010) from PubMed users in the Texas Medical Center, which is the largest medical center in the world. The authors evaluated two document access models based on the work of Anderson and Schooler. The first is based on how frequently a document was accessed. The second is based on both frequency and recency.

The model based only on frequency of past access was highly correlated with the empirical data (R²=0.932), whereas the model based on frequency and recency had a much lower correlation (R²=0.668).

The frequency-only model accurately predicted whether a document will be accessed based on past use. Modeling accesses as a function of frequency requires storing only the number of accesses and the creation date for the document. This model requires low storage overheads and is computationally efficient, making it scalable to large corpora such as MEDLINE.

It is feasible to accurately model the probability of a document being accessed in the future based on past accesses.

Word frequency analysis for ‘values terms’ was performed on citations on diabetes, obesity, dementia, and schizophrenia (Medline; 2004–2006; 4440 citations; 1 110 291 words). Concordance® and SPSS 14.0 were used. Text words and MeSH terms of high frequency and precision were compiled into a search filter. It was validated on datasets of citations on dentistry and food hypersensitivity.

144 unique text words and 124 unique MeSH terms of moderate and high frequency (≥20) and very high precision (≥90%) were identified. Of these, 19 text words and seven MeSH terms were compiled into a ‘brief values filter’. In the derivation dataset, it had a sensitivity of 76.8% and precision of 86.8%. In the validation datasets, its sensitivity and precision were, respectively, 70.1% and 63.6% (food hypersensitivity) and 47.1% and 82.6% (dentistry).

This study provided a varied pool of search terms and a simple and highly effective tool for retrieving publications on health-related values. Further work is required to facilitate access to such research and enhance its chances of being translated into practice, policy, and service improvements.

Markov chain models were built from 2008 web usage data to evaluate the courses' web structure and navigation patterns. The web site was then modified to resolve identified design issues and the improvement in user activity over the subsequent 12 months was quantitatively evaluated.

Web navigation paths were collected between 2008 and 2010. The probability of navigating from one web page to another was analyzed.

The continuing education courses' sequential structure design was clearly reflected in the resulting actual web usage models, and none of the skip transitions provided was heavily used. The web navigation patterns of the two different continuing education courses were similar. Two possible design flaws were identified and fixed in only one of the two courses. Over the following 12 months, the drop-out rate in the modified course significantly decreased from 41% to 35%, but remained unchanged in the unmodified course. The web improvement effects were further verified via a second-order Markov chain model.

The results imply that differences in web content have less impact than web structure design on how learners navigate through continuing education courses. Evaluation of user navigation can help identify web design flaws and guide modifications. This study showed that Markov chain models provide a valuable tool to evaluate web-based education courses. Both the results and techniques in this study would be very useful for public health education and research specialists.