Standardized Health data and Research Exchange (SHaRE): promoting a learning health system.

Aggregate de-identified data from electronic health records (EHRs) provide a valuable resource for research. The Standardized Health data and Research Exchange (SHaRE) is a diverse group of US healthcare organizations contributing to the Cerner Health Facts (HF) and Cerner Real-World Data (CRWD) initiatives. The 51 facilities at the 7 founding organizations have provided data about more than 4.8 million patients with 63 million encounters to HF and 7.4 million patients and 119 million encounters to CRWD. SHaRE organizations unmask their organization IDs and provide 3-digit zip code (zip3) data to support epidemiology and disparity research. SHaRE enables communication between members, facilitating data validation and collaboration as we demonstrate by comparing imputed EHR module usage to actual usage. Unlike other data sharing initiatives, no additional technology installation is required. SHaRE establishes a foundation for members to engage in discussions that bridge data science research and patient care, promoting the learning health system.

A Smartphone-Based Decision Support Tool for Predicting Patients at Risk of Chemotherapy-Induced Nausea and Vomiting: Retrospective Study on App Development Using Decision Tree Induction.

BACKGROUND: Chemotherapy-induced nausea and vomiting (CINV) are the two most frightful and unpleasant side effects of chemotherapy. CINV is accountable for poor treatment outcomes, treatment failure, or even death. It can affect patients' overall quality of life, leading to many social, economic, and clinical consequences. OBJECTIVE: This study compared the performances of different data mining models for predicting the risk of CINV among the patients and developed a smartphone app for clinical decision support to recommend the risk of CINV at the point of care. METHODS: Data were collected by retrospective record review from the electronic medical records used at the University of Missouri Ellis Fischel Cancer Center. Patients who received chemotherapy and standard antiemetics at the oncology outpatient service from June 1, 2010, to July 31, 2012, were included in the study. There were six independent data sets of patients based on emetogenicity (low, moderate, and high) and two phases of CINV (acute and delayed). A total of 14 risk factors of CINV were chosen for data mining. For our study, we used five popular data mining algorithms: (1) naive Bayes algorithm, (2) logistic regression classifier, (3) neural network, (4) support vector machine (using sequential minimal optimization), and (5) decision tree. Performance measures, such as accuracy, sensitivity, and specificity with 10-fold cross-validation, were used for model comparisons. A smartphone app called CINV Risk Prediction Application was developed using the ResearchKit in iOS utilizing the decision tree algorithm, which conforms to the criteria of explainable, usable, and actionable artificial intelligence. The app was created using both the bulk questionnaire approach and the adaptive approach. RESULTS: The decision tree performed well in both phases of high emetogenic chemotherapies, with a significant margin compared to the other algorithms. The accuracy measure for the six patient groups ranged from 79.3% to 94.8%. The app was developed using the results from the decision tree because of its consistent performance and simple, explainable nature. The bulk questionnaire approach asks 14 questions in the smartphone app, while the adaptive approach can determine questions based on the previous questions' answers. The adaptive approach saves time and can be beneficial when used at the point of care. CONCLUSIONS: This study solved a real clinical problem, and the solution can be used for personalized and precise evidence-based CINV management, leading to a better life quality for patients and reduced health care costs.

Building consensus toward a national nursing home information technology maturity model.

OBJECTIVES: We describe the development of a nursing home information technology (IT) maturity model designed to capture stages of IT maturity. MATERIALS AND METHODS: This study had 2 phases. The purpose of phase I was to develop a preliminary nursing home IT maturity model. Phase II involved 3 rounds of questionnaires administered to a Delphi panel of expert nursing home administrators to evaluate the validity of the nursing home IT maturity model proposed in phase I. RESULTS: All participants (n = 31) completed Delphi rounds 1-3. Over the 3 Delphi rounds, the nursing home IT maturity staging model evolved from a preliminary, 5-stage model (stages 1-5) to a 7-stage model (stages 0-6). DISCUSSION: Using innovative IT to improve patient outcomes has become a broad goal across healthcare settings, including nursing homes. Understanding the relationship between IT sophistication and quality performance in nursing homes relies on recognizing the spectrum of nursing home IT maturity that exists and how IT matures over time. Currently, no universally accepted nursing home IT maturity model exists to trend IT adoption and determine the impact of increasing IT maturity on quality. CONCLUSIONS: A 7-stage nursing home IT maturity staging model was successfully developed with input from a nationally representative sample of U.S. based nursing home experts. The model incorporates 7-stages of IT maturity ranging from stage 0 (nonexistent IT solutions or electronic medical record) to stage 6 (use of data by resident or resident representative to generate clinical data and drive self-management).