OBJECTIVES: To describe the prevalence and trends in the use of social media over time and explore whether social media use is related to better self-care efficacy and thus related to better mental health among United States older adults with multimorbidity. MATERIALS AND METHODS: Respondents aged 65 years+ and having 2 or more chronic conditions from the 2017-2020 Health Information National Trends Survey were analyzed (N = 3341) using weighted descriptive and logistic regression analyses. RESULTS: Overall, 48% (n = 1674) of older adults with multimorbidity used social media and there was a linear trend in use over time, increasing from 41.1% in 2017 to 46.5% in 2018, and then further up to 51.7% in 2019, and 54.0% in 2020. Users were often younger, married/partnered, and non-Hispanic White with high education and income. Social media use was associated with better self-care efficacy that was further related to better mental health, indicating a significant mediation effect of self-care efficacy in the relationship between social media use and mental health. DISCUSSION: Although older adults with multimorbidity are a fast-growing population using social media for health, significant demographic disparities exist. While social media use is promising in improving self-care efficacy and thus mental health, relying on social media for the management of multimorbidity might be potentially harmful to those who are not only affected by multimorbidity but also socially disadvantaged (eg, non-White with lower education). CONCLUSION: Great effort is needed to address the demographic disparity and ensure health equity when using social media for patient care.

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Objective: To examine changes in patient outcome variables, length of stay (LOS), and mortality after implementation of computerized provider order entry (CPOE). Materials and Methods: A 5-year retrospective pre-post study evaluated 66 186 patients and 104 153 admissions (49 683 pre-CPOE, 54 470 post-CPOE) at an academic medical center. Generalized linear mixed statistical tests controlled for 17 potential confounders with 2 models per outcome. Results: After controlling for covariates, CPOE remained a significant statistical predictor of decreased LOS and mortality. LOS decreased by 0.90 days, P < .0001. Mortality decrease varied by model: 1 death per 1000 admissions (pre = 0.006, post = 0.0005, P < .001) or 3 deaths (pre = 0.008, post = 0.005, P < .01). Mortality and LOS decreased in medical and surgical units but increased in intensive care units. Discussion: This study examined CPOE at multiple levels. Given the inability to randomize CPOE assignment, these results may only be applicable to the local setting. Temporal trends found in this study suggest that hospital-wide implementations may have impacted nursing staff and new residents. Differences in the results were noted at the patient care unit and room levels. These differences may partly explain the mixed results from previous studies. Conclusion: Controlling for confounders, CPOE implementation remained a statistically significant predictor of LOS and mortality at this site. Mortality appears to be a sensitive outcome indicator with regard to hospital-wide implementations and should be further studied.

OBJECTIVES: To examine the feasibility of deploying a virtual web service for sharing data within a research network, and to evaluate the impact on data consistency and quality. MATERIAL AND METHODS: Virtual machines (VMs) encapsulated an open-source, semantically and syntactically interoperable secure web service infrastructure along with a shadow database. The VMs were deployed to 8 Collaborative Pediatric Critical Care Research Network Clinical Centers. RESULTS: Virtual web services could be deployed in hours. The interoperability of the web services reduced format misalignment from 56% to 1% and demonstrated that 99% of the data consistently transferred using the data dictionary and 1% needed human curation. CONCLUSIONS: Use of virtualized open-source secure web service technology could enable direct electronic abstraction of data from hospital databases for research purposes.

The Arden Syntax is an HL7 standard language for representing medical knowledge as logic statements. Despite nearly 2 decades of availability, Arden Syntax has not been widely used. This has been attributed to the lack of a generally available compiler to implement the logic, to Arden's complex syntax, to the challenges of mapping local data to data references in the Medical Logic Modules (MLMs), or, more globally, to the general absence of decision support in healthcare computing. An XML representation (ArdenML) may partially address the technical challenges. MLMs created in ArdenML can be converted into executable files using standard transforms written in the Extensible Stylesheet Language Transformation (XSLT) language. As an example, we have demonstrated an approach to executing MLMs written in ArdenML using the Drools business rule management system. Extensions to ArdenML make it possible to generate a user interface through which an MLM developer can test for logical errors.

We describe the use of a frame-based knowledge representation to construct an adequately-explicit bedside clinical decision support application for ventilator weaning. The application consists of a data entry form, a knowledge base, an inference engine, and a patient database. The knowledge base contains database queries, a data dictionary, and decision frames. A frame consists of a title, a list of findings necessary to make a decision or carry out an action, and a logic or mathematical statement to determine its output. Frames for knowledge representation are advantageous because they can be created, visualized, and conceptualized as self-contained entities that correspond to accepted medical constructs. They facilitate knowledge engineering and provide understandable explanations of protocol outputs for clinicians. Our frames are elements of a hierarchical decision process. In addition to running diagnostic and therapeutic logic, frames can run database queries, make changes to the user interface, and modify computer variables.