Required data elements for interprofessional rounds through the lens of multiple professions.

The lack of a proper system for ongoing open interprofessional communication among care providers increases miscommunications and medical errors. Seamless access to patient information is important for care providers to prevent miscommunication and improve patient safety. A shared understanding of the information needs of different care providers in an interprofessional team is lacking. Our purpose is to identify care providers' information needs from the perspective of different professions for communication, shared understanding about the patient, and decision-making. We conducted semi-structured interviews with 10 subject matter experts representing eight professions, including dentistry, dietetics, medicine, nursing, occupational therapy, pharmacy, physical therapy, and social work in a 465-bed academic hospital at a large urban Midwestern city. We used an in-house rounding tool presenting physicians' information needs and a hypothetical patient scenario to collect participants' feedback. Interview notes were coded using direct content analysis. We identified 22 additional essential data elements for an interprofessional rounding tool. We categorized those into six domains: discharge-related, social determinants of health, hospital safety, nutrition, interprofessional situation awareness, and patient history. A well-designed validated rounding tool that includes an interprofessional team of care providers' information needs could improve communication, care planning, and decision-making among them.

Developing a portable natural language processing based phenotyping system.

BACKGROUND: This paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches. METHODS: Our system utilizes UMLS to extract clinically relevant features from the unstructured text and then facilitates portability across different institutions and data systems by incorporating OHDSI's OMOP Common Data Model (CDM) to standardize necessary data elements. Our system can also store the key components of rule-based systems (e.g., regular expression matches) in the format of OMOP CDM, thus enabling the reuse, adaptation and extension of many existing rule-based clinical NLP systems. We experimented with our system on the corpus from i2b2's Obesity Challenge as a pilot study. RESULTS: Our system facilitates portable phenotyping of obesity and its 15 comorbidities based on the unstructured patient discharge summaries, while achieving a performance that often ranked among the top 10 of the challenge participants. CONCLUSION: Our system of standardization enables a consistent application of numerous rule-based and machine learning based classification techniques downstream across disparate datasets which may originate across different institutions and data systems.

Effectiveness of Mobile Health Technology Interventions for Patients With Heart Failure: Systematic Review and Meta-analysis.

BACKGROUND: Heart failure (HF) is a complex and serious condition associated with substantial morbidity, mortality, and health care costs. We conducted a systematic review and meta-analysis to evaluate the effects of mobile health (mHealth) interventions compared with usual care in patients with HF. METHODS: We searched MEDLINE, CENTRAL, CINAHL, and EMBASE databases to identify eligible randomized controlled trials (RCTs) of mHealth interventions. Primary outcomes included: all-cause mortality, cardiovascular mortality, HF-related hospitalizations, and all-cause hospitalizations. Meta-analyses using a random effects model were performed for all outcomes. Risk of bias and quality of evidence were evaluated using the Cochrane Tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. RESULTS: Sixteen RCTs involving 4389 patients were included. Compared with usual care, mHealth interventions reduced the risk of all-cause mortality (risk ratio [RR], 0.80; 95% confidence interval [CI], 0.65-0.97; absolute risk reduction [ARR], 2.1%; high-quality evidence), cardiovascular mortality (RR, 0.70; 95% CI, 0.53-0.91; ARR, 2.9%; high-quality evidence), and HF hospitalizations (RR, 0.77; 95% CI, 0.67-0.88; ARR, 5%; high-quality evidence), but had no effect on all-cause hospitalizations. Results were driven by mHealth interventions with remote monitoring and clinical feedback, which were associated with larger reductions than stand-alone mHealth interventions. However, subgroup differences were not statistically significant. CONCLUSIONS: mHealth interventions with remote monitoring and clinical feedback reduce mortality and HF-related hospitalizations, but might not reduce all-cause hospitalizations in patients with HF. Additional studies are needed to determine the efficacy of stand-alone mHealth interventions as well as active features of mHealth that contribute to efficacy.

Effects of CPOE-based medication ordering on outcomes: an overview of systematic reviews.

BACKGROUND: Computerised provider order entry (CPOE) systems are widely used in clinical settings for the electronic ordering of medications, laboratory tests and radiological therapies. However, evidence regarding effects of CPOE-based medication ordering on clinical and safety outcomes is mixed. We conducted an overview of systematic reviews (SRs) to characterise the cumulative effects of CPOE use for medication ordering in clinical settings. METHODS: MEDLINE, EMBASE, CINAHL and the Cochrane Library were searched to identify published SRs from inception to 12 February 2018. SRs investigating the effects of the use of CPOE for medication ordering were included. Two reviewers independently extracted data and assessed the methodological quality of included SRs. RESULTS: Seven SRs covering 118 primary studies were included for review. Pooled studies from the SRs in inpatient settings showed that CPOE use resulted in statistically significant decreases in medication errors and adverse drug events (ADEs); however, there was considerable variation in the magnitude of their relative risk reduction (54%-92% for errors, 35%-53% for ADEs). There was no significant relative risk reduction on hospital mortality or length of stay. Bibliographic analysis showed limited overlap (24%) among studies included across all SRs. CONCLUSION: SRs on CPOEs included predominantly non-randomised controlled trials and observational studies with varying foci. SRs predominantly focused on inpatient settings and often lacked comparison groups; SRs used inconsistent definitions of outcomes, lacked descriptions regarding the effects on patient harm and did not differentiate among the levels of available decision support. With five of the seven SRs having low to moderate quality, findings from the SRs must be interpreted with caution. We discuss potential directions for future primary studies and SRs of CPOE.