Abstract Maternal and infant health (MIH) mobile applications (apps) are increasingly popular and frequently used for health education and decision making. Interventions grounded in theory-based behavior change techniques (BCTs) are shown to be effective in promoting healthy behavior changes. MIH apps have the potential to be useful tools, yet the extent to which they incorporate BCTs is still unknown. The objective of this study was to assess the presence of BCTs in popular MIH apps available in the Apple App and Google Play stores. Twenty-nine popular MIH apps were coded for the presence of 16 BCTs using the mHealth app taxonomy. Popular MIH apps whose purpose was to provide health education or decision-making support to pregnant women or parents/caregivers of infants were included in the final sample. On an average, the reviewed apps included seven BCTs (range 2–16). Techniques such as personalization, review of general or specific goals, macro tailoring, self-monitoring of goals, and health behavior linkages were most frequently present. No differences in the presence of BCTs between paid and free apps were observed. Popular MIH apps typically included only a minority of BCTs found to be useful for health promotion. However, apps developed by healthcare developers incorporated a higher number of BCTs within the app content. Therefore, app developers and policymakers may consider strategies to increase health expert involvement in app design and content delivery.
Of the different medical classification systems ICD only has endured on the long time, and through the german government regulations from 1985 (Bundespflegesatzordnung) ICD has become a must to all hospital departments for administration purposes. As a clinical and scientific system for encoding of diagnosis and treatment procedures ICD has been proved to be of minor suitability. SNOMED, a medical classification system derived from a pathological description of diseases seemed first to fulfill all the wishes clinicians have; but the very little use over the 12 years since its publication demonstrates its poor acceptance. This might be due to the time consuming and lavish procedure of documentation in SNOMED, as entries have to be made in 7 categories. DocuMed, which is a microcomputer program and database on the one hand and a medical classification system on the other, seems to provide interesting features. For minimal documentation it only needs entries in the category of diagnosis, a term not present in SNOMED; for more detailed documentation requirements DocuMed provides similar categories as SNOMED with references to the latter and to ICD.
The objective of this study was to identify factors associated with hospitals that achieved the Medicare meaningful use incentive thresholds for payment under the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. We employed a cross-sectional design using data from the 2011 American Hospital Association Annual Survey, including the Information Technology Supplement; the Centers for Medicare & Medicaid Services report of hospitals receiving meaningful use payments; and the Health Resources and Services Administration's Area Resource File. We used a lagged value from 2010 to determine electronic health record (EHR) adoption. Our methods were a descriptive analysis and logistic regression to examine how various hospital characteristics are associated with the achievement of Medicare meaningful use incentives. Overall, 1,769 (38%) of 4,683 potentially eligible hospitals achieved meaningful use incentive thresholds by the end of 2012. Characteristics associated with organizations that received incentive payments were having an EHR in place in 2010, having a larger bed size, having a single health information technology vendor, obtaining Joint Commission accreditation, operating under for-profit status, having Medicare share of inpatient days in the middle two quartiles, being eligible for Medicaid incentives, and being located in the Middle Atlantic or South Atlantic census region. Characteristics associated with not receiving incentive payments were being a member of a hospital system and being located in the Mountain or Pacific census region. Thus far, little evidence suggests that the HITECH incentive program has enticed hospitals without an EHR system to adopt meaningful use criteria. Policy makers should consider modifying the incentive program to accelerate the adoption of and meaningful use in hospitals without EHRs.
Objective. The COVID-19 pandemic created an unprecedented strain on the health care system, and administrators had to make many critical decisions to respond appropriately. This study sought to understand how health care administrators used data and information for decision making during the first 6 mo of the COVID-19 pandemic. Materials and Methods. We conducted semistructured interviews with administrators across University of Florida (UF) Health. We performed an inductive thematic analysis of the transcripts. Results. Four themes emerged from the interviews: 1) common types of health systems or hospital operations data; 2) public health and other external data sources; 3) data interaction, integration, and exchange; and 4) novelty and evolution in data, information, or tools used over time. Participants illustrated the organizational, public health, and regional information they considered essential (e.g., hospital census, community positivity rate, etc.). Participants named specific challenges they faced due to data quality and timeliness. Participants elaborated on the necessity of data integration, validation, and coordination across different boundaries (e.g., different hospital systems in the same metro areas, public health agencies at the local, state, and federal level, etc.). Participants indicated that even within the first 6 mo of the COVID-19 pandemic, the data and tools used for making critical decisions changed. Discussion. While existing medical informatics infrastructure can facilitate decision making in pandemic response, data may not always be readily available in a usable format. Interoperable infrastructure and data standardization across multiple health systems would help provide more reliable and timely information for decision making. Conclusion. Our findings contribute to future discussions of improving data infrastructure and developing harmonized data standards needed to facilitate critical decisions at multiple health care system levels.The study revealed common health systems or hospital operations data and information used in decision making during the first 6 mo of the COVID-19 pandemic.Participants described commonly used internal data sources, such as resource and financial reports and dashboards, and external data sources, such as federal, state, and local public health data.Participants described challenges including poor timeliness and limited local relevance of external data as well as poor integration of data sources within and across organizational boundaries.Results suggest the need for continued integration and standardization of health data to support health care administrative decision making during pandemics or other emergencies.
BackgroundGiven their ability to process highly dimensional datasets with hundreds of variables, machine learning algorithms may offer one solution to the vexing challenge of predicting postoperative pain.
The number of patients tapered from long-term opioid therapy (LTOT) has increased in recent years in the United States. Some patients tapered from LTOT report improved quality of life, while others face increased risks of opioid-related hospital use. Research has not yet established how the risk of opioid-related hospital use changes across LTOT dose and subsequent tapering. Our objective was to examine associations between recent tapering from LTOT with odds of opioid-related hospital use.Case-crossover design using 2014-2018 health information exchange data from Indiana. We defined opioid-related hospital use as hospitalizations, and emergency department (ED) visits for a drug overdose, opioid abuse, and dependence. We defined tapering as a 15% or greater dose reduction following at least 3 months of continuous opioid therapy of 50 morphine milligram equivalents (MME)/day or more. We used conditional logistic regression to estimate odds ratios (OR) with 95% confidence intervals (CI).Recent tapering from LTOT was associated with increased odds of opioid-related hospital use (OR: 1.50, 95%CI: 1.34-1.63), ED visit (OR: 1.52; 95%CI: 1.35-1.72), and inpatient hospitalization (OR: 1.40; 95%CI: 1.20-1.65). We found no evidence of heterogeneity of the effect of tapering on opioid-related hospital use by gender, age, and race. Recent tapering among patients on a high baseline dose (>300 MME) was associated with increased odds of opioid-related hospital use (OR: 2.95, 95% CI: 2.12-4.11, p < 0.001) compared to patients on a lower baseline doses.Recent tapering from LTOT is associated with increased odds of opioid-related hospital use.
