Applied Clinical Informatics 

About: Applied Clinical Informatics is an academic journal published by Schattauer Verlag. The journal publishes majorly in the area(s): Medicine & Health care. It has an ISSN identifier of 1869-0327. Over the lifetime, 1093 publications have been published receiving 14601 citations. The journal is also known as: ACI ; official eJournal of IMIA and AMDIS & ACI.

A Systematic Review of the Technology Acceptance Model in Health Informatics.

Abstract: Background One common model utilized to understand clinical staff and patients' technology adoption is the technology acceptance model (TAM). Objective This article reviews published research on TAM use in health information systems development and implementation with regard to application areas and model extensions after its initial introduction. Method An electronic literature search supplemented by citation searching was conducted on February 2017 of the Web of Science, PubMed, and Scopus databases, yielding a total of 492 references. Upon eliminating duplicates and applying inclusion and exclusion criteria, 134 articles were retained. These articles were appraised and divided into three categories according to research topic: studies using the original TAM, studies using an extended TAM, and acceptance model comparisons including the TAM. Results The review identified three main information and communication technology (ICT) application areas for the TAM in health services: telemedicine, electronic health records, and mobile applications. The original TAM was found to have been extended to fit dynamic health service environments by integration of components from theoretical frameworks such as the theory of planned behavior and unified theory of acceptance and use of technology, as well as by adding variables in specific contextual settings. These variables frequently reflected the concepts subjective norm and self-efficacy, but also compatibility, experience, training, anxiety, habit, and facilitators were considered. Conclusion Telemedicine applications were between 1999 and 2017, the ICT application area most frequently studied using the TAM, implying that acceptance of this technology was a major challenge when exploiting ICT to develop health service organizations during this period. A majority of the reviewed articles reported extensions of the original TAM, suggesting that no optimal TAM version for use in health services has been established. Although the review results indicate a continuous progress, there are still areas that can be expanded and improved to increase the predictive performance of the TAM.

Telemedicine for Developing Countries. A Survey and Some Design Issues.

Abstract: Background: Developing countries need telemedicine applications that help in many situations, when physicians are a small number with respect to the population, when specialized physicians are not available, when patients and physicians in rural villages need assistance in the delivery of health care. Moreover, the requirements of telemedicine applications for developing countries are somewhat more demanding than for developed countries. Indeed, further social, organizational, and technical aspects need to be considered for successful telemedicine applications in developing countries. Objective: We consider all the major projects in telemedicine, devoted to developing countries, as described by the proper scientific literature. On the basis of such literature, we want to define a specific taxonomy that allows a proper classification and a fast overview of telemedicine projects in developing countries. Moreover, by considering both the literature and some recent direct experiences, we want to complete such overview by discussing some design issues to be taken into consideration when developing telemedicine software systems. Methods: We considered and reviewed the major conferences and journals in depth, and looked for reports on the telemedicine projects. Results: We provide the reader with a survey of the main projects and systems, from which we derived a taxonomy of features of telemedicine systems for developing countries. We also propose and discuss some classification criteria for design issues, based on the lessons learned in this research area. Conclusions: We highlight some challenges and recommendations to be considered when designing a telemedicine system for developing countries.

Drug Interaction Alert Override Rates in the Meaningful Use Era: No Evidence of Progress

Abstract: Background: Interruptive drug interaction alerts may reduce adverse drug events and are required for Stage I Meaningful Use attestation. For the last decade override rates have been very high. Despite their widespread use in commercial EHR systems, previously described interventions to improve alert frequency and acceptance have not been well studied. Objectives: (1) To measure override rates of inpatient medication alerts within a commercial clinical decision support system, and assess the impact of local customization efforts. (2) To compare override rates between drug-drug interaction and drug-allergy interaction alerts, between attending and resident physicians, and between public and academic hospitals. (3) To measure the correlation between physicians’ individual alert quantities and override rates as an indicator of potential alert fatigue. Methods: We retrospectively analyzed physician responses to drug-drug and drug-allergy interaction alerts, as generated by a common decision support product in a large teaching hospital system. Results: (1) Over four days, 461 different physicians entered 18,354 medication orders, resulting in 2,455 visible alerts; 2,280 alerts (93%) were overridden. (2) The drug-drug alert override rate was 95.1%, statistically higher than the rate for drug-allergy alerts (90.9%) (p Conclusions: Despite intensive efforts to improve a commercial drug interaction alert system and to reduce alerting, override rates remain as high as reported over a decade ago. Alert fatigue does not seem to contribute. The results suggest the need to fundamentally question the premises of drug interaction alert systems. Citation: Bryant AD, Fletcher GS, Payne TH. Drug interaction alert override rates in the Meaningful Use era: No evidence of progress. Appl Clin Inf 2014; 5: 802–813 http://dx.doi.org/10.4338/ACI-2013-12-RA-0103

Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

Abstract: Background: Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity. Objective: To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows. Methods: Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost. Results: From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods. Conclusions: This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient’s show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity. Citation: Huang Y, Hanauer D.A. Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl Clin Inf 2014; 5: 836–860 http://dx.doi.org/10.4338/ACI-2014-04-RA-0026

Responding to COVID-19: The UW Medicine Information Technology Services Experience.

Abstract: Background UW Medicine was one of the first health systems to encounter and treat COVID-19 patients in the United States, starting in late February 2020. Objective Here we describe the rapid rollout of capabilities by UW Medicine Information Technology Services (ITS) to support our clinical response to the COVID-19 pandemic and provide recommendations for health systems to urgently consider, as they plan their own response to this and potentially other future pandemics. Methods Our recommendations include establishing a hospital incident command structure that includes tight integration with IT, creating automated dashboards for incident command, optimizing emergency communication to staff and patients, and preparing human resources, security, other policies, and equipment to support the transition of all nonessential staff to telework. We describe how UW Medicine quickly expanded telemedicine capabilities to include most primary care providers and increasing numbers of specialty providers. We look at how we managed expedited change control processes to quickly update electronic health records (EHR) with new COVID-19 laboratory and clinical workflows. We also examine the integration of new technology such as tele–intensive care (ICU) equipment and improved integration with teleconferencing software into our EHR. To support the rapid preparation for COVID-19 at other health systems, we include samples of the UW Medicine's COVID-19 order set, COVID-19 documentation template, dashboard metric categories, and a list of the top 10 things your health care IT organization can do now to prepare. Conclusion The COVID-19 response requires new and expedited ways of approaching ITS support to clinical needs. UW Medicine ITS leadership hope that by quickly sharing our nimble response to clinical and operational requests, we can help other systems prepare to respond to this public health emergency.