Closing the Digital Health Evidence Gap: Development of a Predictive Score to Maximize Patient Outcomes.

Background: Clinical studies of telemedicine (TM) programs for chronic illness have demonstrated mixed results across settings and populations. With recent uptake in use of digital health modalities, more precise patient classification may improve outcomes, efficiency, and effectiveness. Objective: The purpose of the research was to develop a predictive score that measures the influence of patient characteristics on TM interventions. The central hypothesis is that disease type, illness severity, and the social determinants of health influence outcomes, including resource utilization, and can be precisely characterized. Methods: The retrospective study evaluated the feasibility of creating a patient "Telemedicine ImPact" (TIP) score derived from a Virginia Medicare and Medicaid claims data set. Claims were randomly selected, stratified by disease type, and matched by illness severity into a TM intervention group (N = 7,782) and a nontelemedicine "usual care" control cohort (N = 7,981). The individual records were then summarized into 15,762 cases with 80% of the cases used to develop, train, and test four predictive models (hospital utilization, readmissions, total utilization, and mortality) using 10-fold cross-validation. Results: Bayesian supervised machine learning achieved reference model performance index area under the curve for receiver operating characteristic (AUC/ROC) ≥0.85. Posterior probabilities for each outcome model were generated on a "hold-back" set of 3,082 cases. Robust parametric statistical methods enabled dimension reduction, model validation, and derivation of a reliable composite scaled score that quantified the overall health risk for each case. The TM intervention cohort demonstrated higher total utilization (representing the sum of inpatient, outpatient, and prescription use) and lower mean inpatient utilization than the usual standard of care. This finding suggests TM-based care may shift the composition of health resource utilization, reducing hospitalizations while increasing outpatient services, adjusted for patient differences. Conclusions: The creation of a patient score using machine learning to predict the effect of TM on outcomes is feasible. Adoption of the TIP score may reduce variability in results by more precisely accounting for the effects of patient characteristics on health outcomes and utilization. More consistent outcome prediction may lead to greater support for digital health.

Remote Health Monitoring for Older Adults and Those with Heart Failure: Adherence and System Usability.

BACKGROUND: Remote health monitoring technology has been suggested as part of an early intervention and prevention care model. Older adults with a chronic health condition have been shown to benefit from remote monitoring but often have challenges with complex technology. The current study reports on the usability of and adherence with an integrated, real-time monitoring system over an extended period of time by older adults with and without a chronic health condition. MATERIALS AND METHODS: Older adults 55 years of age and over with and without heart failure participated in a study in which a telehealth system was used for 6 months each. The system consisted of a wireless wristwatch-based monitoring device that continuously collected temperature and motion data. Other health information was collected daily using a weight scale, blood pressure cuff, and tablet that participants used for health surveys. Data were automatically analyzed and summarized by the system and presented to study nurses. RESULTS: Forty-one older adults participated. Seventy-one percent of surveys, 75% of blood pressure readings, and 81% of daily weight measurements were taken. Participants wore the watch monitor 77% of the overall 24/7 time requested. The weight scale had the highest usability rating in both groups. The groups did not otherwise differ on device usage. CONCLUSIONS: The findings indicate that a health monitoring system designed for older adults can and will be used for an extended period of time and may help older adults with chronic conditions reside longer in their own homes in partnership with the healthcare system.

Using principal component analysis to aid bayesian network development for prediction of critical care patient outcomes.

BACKGROUND: Predicting an intensive care unit patient's outcome is highly desirable. An end goal is for computational techniques to provide updated, accurate predictions about changing patient condition using a manageable number of physiologic parameters. METHODS: Principal component analysis was used to select input parameters for critical care patient outcome models. Vital signs and laboratory values from each patient's hospital stay along with outcomes ("Discharged" vs. "Deceased") were collected retrospectively at a Level I Trauma-Military Medical Center in the southwest; intensive care unit patients were included if they had been admitted for burn, infection, or hypovolemia during a 5-year period ending October 2007. Principal component analysis was used to determine which of the 24 parameters would serve as inputs in a bayesian network developed for outcome prediction. RESULTS: Data for 581 patients were collected. Pulse pressure, heart rate, temperature, respiratory rate, sodium, and chloride were found to have statistically significant differences between Discharged and Deceased groups for "Hypovolemia" patients. For "Burn" patients, pulse pressure, hemoglobin, hematocrit, and potassium were found to have statistically significant differences. For a "Combined" group, heart rate, temperature, respiratory rate, sodium, and chloride had statistically significant differences. A bayesian network based on these results, developed for the Combined group, achieved an accuracy of 75% when predicting patient outcome. CONCLUSIONS: Outcome prediction for critical care patients is possible. Future work should explore model development using additional temporal data and should include prospective validation. Such technology could serve as the basis of real-time intelligent monitoring systems for critical patients.

The Preventing Suicide Network: Delivering online tailored resources to those who help others.

Concerned over the growing epidemic of death by suicide in the United States, the National Institute of Mental Health of the U.S. National Institutes of Health funded Small Business Innovation Research (SBIR) projects using innovative web-based approaches to provide resources to professionals and the general public about suicide prevention. The Preventing Suicide Network (PSN) was funded (SBIR Contract #N44MH22044) and developed over a three and a half year period (2001-2005) as part of this initiative. The PSN provides intermediaries (those who participate in activities to prevent suicide) with an online community dedicated to timely access to authoritative and problem-specific tailored information.

Telehealth practice in home care. Synopsis of the 2000 US Telenursing role study.

Any use and integration of new technology into nursing should be accomplished in a purposeful and meaningful manner. The synopsis reported here provides the health care arena with nurses' response to devices and technologies, their emerging roles in the health care system as a result of telehealth technology, and the impact of new technology on patients and health care enterprises.

Report of the 2004-2005 International Telenursing Survey.

This article reports the findings of a 2004-2005 survey, which portrays an international sample of nurses (n = 719) working with telehealth technologies. The respondents came from a total of 36 countries. The majority of the respondents were from the US, representing 49 of 50 states. The survey provides important information about the characteristics of telenurses; the satisfaction of telenurses with their current telenursing role; the specific knowledge and skills used by telenurses; the settings in which knowledge, skills, and competencies were developed; telenurses' opinions on education in telehealth; telenurses' perceptions about the effectiveness of telenursing in making an impact on the nursing shortage; and the demand for telenurses worldwide.