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Introduction: The U.S. Army medics are often the first responders in the care of sick and injured soldiers on the battlefield, sick call in a Role 2 aid station and garrison clinics. Sick call medics are required to utilize the Algorithm Directed Troop Medical Care (ADTMC) to care for and then render a disposition for these soldiers. The current ADTMC manual is a thick, heavy paper manual. A desktop and smartphone application has been developed that contains the entire ADTMC manual algorithm-based content. Our goal is to enhance the medics' clinical learning and critical thinking skills while improving their evaluation, disposition, and documentation during patient encounters.Materials and Methods: The application was field-tested with the 173rd IBCT (A) while attending a field exercise at Grafenwoehr, Germany. At the unit's Role 2 tent setup, the use of the paper manual to the utilization of the same material via the ADTMC application was compared by observing the medics' workflows directly while they were caring for ill and injured soldiers.Results: Medics, while examining patients, would demonstrate 50% faster exam time using the application compared to medics using only the manual. Moreover, a 50% decrease in document processing time as a result of digitization of the paper documentation process was confirmed. The application ("app") enabled the medics to continue their screening assessments and patient disposition duties on a desktop computer or a smartphone without the need to refer to the paper manual.Conclusion: Medics, by adopting this tool, will become quicker, more efficient, and develop critical thinking skills. In other words, the ability to objectively evaluate patients in order to form a proper disposition of sick and injured soldiers during training, in the field, as well as in garrison. When utilized properly, the ADTMC application ensures that soldiers reporting to sick call are expeditiously routed to the appropriate level of care, and is a vehicle for further training for medics in the care of soldiers. [ABSTRACT FROM AUTHOR]

Introduction: Quality of life is included in the economic evaluation of health services by measuring the preference for health states, i.e. health state utilities. However, most intervention studies include a disease-specific, not a utility, instrument. Consequently, there has been increasing use of statistical mapping algorithms which permit utilities to be estimated from a disease-specific instrument. The present paper provides such algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and six multi-attribute utility (MAU) instruments, the Euroqol (EQ-5D), the Short Form 6D (SF-6D), the Health Utilities Index (HUI) 3, the Quality of Wellbeing (QWB), the 15D (15 Dimension) and the Assessment of Quality of Life (AQoL-8D). Methods: Heart disease patients and members of the healthy public were recruited from six countries. Non-parametric rank tests were used to compare subgroup utilities and MacNew scores. Mapping algorithms were estimated using three separate statistical techniques. Results: Mapping algorithms achieved a high degree of precision. Based on the mean absolute error and the intra class correlation the preferred mapping is MacNew into SF-6D or 15D. Using the R squared statistic the preferred mapping is MacNew into AQoL-8D. Implications for research: The algorithms reported in this paper enable MacNew data to be mapped into utilities predicted from any of six instruments. This permits studies which have included the MacNew to be used in cost utility analyses which, in turn, allows the comparison of services with interventions across the health system. [ABSTRACT FROM AUTHOR]