Lincoln Sheets

A Systematic Review of Healthcare Applications for Smartphones

BackgroundAdvanced mobile communications and portable computation are now combined in handheld devices called “smartphones”, which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category.MethodsIn April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches.ResultsA total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students.ConclusionsMany medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.

Usability of Selected Databases for Low-Resource Clinical Decision Support

BACKGROUND Smartphones are increasingly important for clinical decision support, but smartphone and Internet use are limited by cost or coverage in many settings. txt2MEDLINE provides access to published medical evidence by text messaging. Previous studies have evaluated this approach, but we found no comparisons with other tools in this format. OBJECTIVES To compare txt2MEDLINE with other databases for answering clinical queries by text messaging in low-resource settings. METHODS Using varied formats, we searched txt2MEDLINE and five other search portals (askMEDLINE, Cochrane, DynaMed, PubMed PICO, and UpToDate) to develop optimal strategies for each. We then searched each database again with five benchmark queries, using the customized search-optimization formats. We truncated the results to less than 480 characters each to simulate delivering them to a maximum of three text messages. Clinicians with practice experience in low-resource areas scored the results on a 5-point Likert scale. RESULTS Median scores and standard deviations from 17 reviewers were: txt2M2MEDLINE, 3.2±0.82 (control); askMEDLINE, 3.2±0.90 (p = 0.918); Cochrane, 3.8±0.58 (p = 0.073); DynaMed, 3.6±0.65 (p = 0.105); PubMed PICO, 3.6±0.82 (p = 0.005); and UpToDate, 4.0±0.52 (p = 0.002). Our sample size was sufficiently powered to find differences of 1.0 point. CONCLUSIONS Comparing several possible sources for texting-based clinical-decision-support information, our results did not demonstrate one-point differences in usefulness on a scale of 1 to 5. PubMed PICO and UpToDate were significantly better than txt2MEDLINE, but with relatively small improvements in Likert score (0.4 and 0.8, respectively). In a texting-only setting, txt2MEDLINE is comparable to simulated alternatives based on established reference sources.

Do Language Fluency and Other Socioeconomic Factors Influence the Use of PubMed and MedlinePlus

BACKGROUND Increased usage of MedlinePlus by Spanish-speakers was observed after introduction of MedlinePlus in Spanish. This probably reflects increased usage of MEDLINE and PubMed by those with greater fluency in the language in which it is presented; but this has never been demonstrated in English speakers. Evidence that lack of English fluency deters international healthcare personnel from using PubMed could support the use of multi-language search tools like Babel-MeSH. OBJECTIVES This study aims to measure the effects of language fluency and other socioeconomic factors on PubMed MEDLINE and MedlinePlus access by international users. METHODS We retrospectively reviewed server pageviews of PubMed and MedlinePlus from various periods of time, and analyzed them against country statistics on language fluency, GDP, literacy rate, Internet usage, medical schools, and physicians per capita, to determine whether they were associated. RESULTS We found fluency in English to be positively associated with pageviews of PubMed and MedlinePlus in countries with high literacy rates. Spanish was generally found to be positively associated with pageviews of MedlinePlus en Español. The other parameters also showed varying degrees of association with pageviews. CONCLUSIONS After adjusting for the other factors investigated in this study, language fluency was a consistently significant predictor of the use of PubMed, MedlinePlus English and MedlinePlus en Español. This study may support the need for multi-language search tools and may increase access of health information resources from non-English speaking countries.

Combining Contrast Mining with Logistic Regression To Predict Healthcare Utilization in a Managed Care Population

BACKGROUND Because 5% of patients incur 50% of healthcare expenses, population health managers need to be able to focus preventive and longitudinal care on those patients who are at highest risk of increased utilization. Predictive analytics can be used to identify these patients and to better manage their care. Data mining permits the development of models that surpass the size restrictions of traditional statistical methods and take advantage of the rich data available in the electronic health record (EHR), without limiting predictions to specific chronic conditions. OBJECTIVE The objective was to demonstrate the usefulness of unrestricted EHR data for predictive analytics in managed healthcare. METHODS In a population of 9,568 Medicare and Medicaid beneficiaries, patients in the highest 5% of charges were compared to equal numbers of patients with the lowest charges. Contrast mining was used to discover the combinations of clinical attributes frequently associated with high utilization and infrequently associated with low utilization. The attributes found in these combinations were then tested by multiple logistic regression, and the discrimination of the model was evaluated by the c-statistic. RESULTS Of 19,014 potential EHR patient attributes, 67 were found in combinations frequently associated with high utilization, but not with low utilization (support>20%). Eleven of these attributes were significantly associated with high utilization (p<0.05). A prediction model composed of these eleven attributes had a discrimination of 84%. CONCLUSIONS EHR mining reduced an unusably high number of patient attributes to a manageable set of potential healthcare utilization predictors, without conjecturing on which attributes would be useful. Treating these results as hypotheses to be tested by conventional methods yielded a highly accurate predictive model. This novel, two-step methodology can assist population health managers to focus preventive and longitudinal care on those patients who are at highest risk for increased utilization.