STRIPA: A Rule-Based Decision Support System for Medication Reviews in Primary Care

The chronic use of multiple medicinal drugs is growing, partly because individual patients’ drugs have not been adequately prescribed by primary care physicians. In order to reduce these polypharmacy problems, the Systematic Tool to Reduce Inappropriate Prescribing (STRIP) has been created. To facilitate physicians’ use of the STRIP method, the STRIP Assistant (STRIPA) has been developed. STRIPA is a stand-alone web-based decision support system that advices physicians during the pharmacotherapeutic analysis of patients’ health records. In this paper the application’s architecture and rule engine, and the design decisions relating to the user interface and semantic interoperability, are described. An experimental validation of the prototype by general practitioners and pharmacists showed that users perform significantly better when optimizing medication with STRIPA than without. This leads the authors to believe that one process-oriented decision support system, built around a context-aware rule engine, operated through an intuitive user interface, is able to contribute to improving drug prescription practices

A mixed methods analysis of the medication review intervention centered around the use of the ‘Systematic Tool to Reduce Inappropriate Prescribing’ Assistant (STRIPA) in Swiss primary care practices

Background: Electronic clinical decision support systems (eCDSS), such as the ‘Systematic Tool to Reduce Inappropriate Prescribing’ Assistant (STRIPA), have become promising tools for assisting general practitioners (GPs) with conducting medication reviews in older adults. Little is known about how GPs perceive eCDSS-assisted recommendations for pharmacotherapy optimization. The aim of this study was to explore the implementation of a medication review intervention centered around STRIPA in the ‘Optimising PharmacoTherapy In the multimorbid elderly in primary CAre’ (OPTICA) trial. Methods: We used an explanatory mixed methods design combining quantitative and qualitative data. First, quantitative data about the acceptance and implementation of eCDSS-generated recommendations from GPs (n = 21) and their patients (n = 160) in the OPTICA intervention group were collected. Then, semi-structured qualitative interviews were conducted with GPs from the OPTICA intervention group (n = 8), and interview data were analyzed through thematic analysis. Results: In quantitative findings, GPs reported averages of 13 min spent per patient preparing the eCDSS, 10 min performing medication reviews, and 5 min discussing prescribing recommendations with patients. On average, out of the mean generated 3.7 recommendations (SD=1.8). One recommendation to stop or start a medication was reported to be implemented per patient in the intervention group (SD=1.2). Overall, GPs found the STRIPA useful and acceptable. They particularly appreciated its ability to generate recommendations based on large amounts of patient information. During qualitative interviews, GPs reported the main reasons for limited implementation of STRIPA were related to problems with data sourcing (e.g., incomplete data imports), preparation of the eCDSS (e.g., time expenditure for updating and adapting information), its functionality (e.g., technical problems downloading PDF recommendation reports), and appropriateness of recommendations. Conclusions: Qualitative findings help explain the relatively low implementation of recommendations demonstrated by quantitative findings, but also show GPs’ overall acceptance of STRIPA. Our results provide crucial insights for adapting STRIPA to make it more suitable for regular use in future primary care settings (e.g., necessity to improve data imports). Trial registration: Clinicaltrials.gov NCT03724539, date of first registration: 29/10/2018

Efficiency of Clinical Decision Support Systems Improves with Experience

Efficiency, or the resources spent while performing a specific task, is widely regarded as one the determinants of usability. In this study, the authors hypothesize that having a group of users perform a similar task over a prolonged period of time will lead to improvements in efficiency of that task. This study was performed in the domain of decision-supported medication reviews. Data was gathered during a randomized controlled trial. Three expert teams consisting of an independent physician and an independent pharmacist conducted 150 computerized medication reviews on patients in 13 general practices located in Amsterdam, the Netherlands. Results were analyzed with a linear mixed model. A fixed effects test on the linear mixed model showed a significant difference in the time required to conduct medication reviews over time; F(31.145) = 14.043, p < .001. The average time in minutes required to conduct medication reviews up to the first quartile was M = 20.42 (SD = 9.00), while the time from the third quartile up was M = 9.81 (SD = 6.13). This leads the authors to conclude that the amount of time users needed to perform similar tasks decreased significantly as they gained experience over time

Computerized Decision Support Improves Medication Review Effectiveness : An Experiment Evaluating the STRIP Assistant's Usability

BACKGROUND: Polypharmacy poses threats to patients' health. The Systematic Tool to Reduce Inappropriate Prescribing (STRIP) is a drug optimization process for conducting medication reviews in primary care. To effectively and efficiently incorporate this method into daily practice, the STRIP Assistant--a decision support system that aims to assist physicians with the pharmacotherapeutic analysis of patients' medical records--has been developed. It generates context-specific advice based on clinical guidelines. OBJECTIVE: The aim of this study was to validate the STRIP Assistant's usability as a tool for physicians to optimize medical records for polypharmacy patients. METHODS: In an online experiment, 42 physicians were asked to optimize medical records for two comparable polypharmacy patients, one in their usual manner and one using the STRIP Assistant. Changes in effectiveness were measured by comparing respondents' optimized medicine prescriptions with medication prepared by an expert panel of two geriatrician-pharmacologists. Efficiency was operationalized by recording the time the respondents took to optimize the two cases. User satisfaction was measured with the System Usability Scale (SUS). Independent and paired t tests were used for analysis. RESULTS: Medication optimization significantly improved with the STRIP Assistant. Appropriate decisions increased from 58% without the STRIP Assistant to 76% with it (p < 0.0001). Inappropriate decisions decreased from 42% without the STRIP Assistant to 24% with it (p < 0.0001). Participants spent significantly more time optimizing medication with the STRIP Assistant (24 min) than without it (13 min; p < 0.0001). They assigned it a below-average SUS score of 63.25. CONCLUSION: The STRIP Assistant improves the effectiveness of medication reviews for polypharmacy patients

The Systematic Tool to Reduce Inappropriate Prescribing (STRIP) : Combining implicit and explicit prescribing tools to improve appropriate prescribing

Inappropriate prescribing is a major health care issue, especially regarding older patients on polypharmacy. Multiple implicit and explicit prescribing tools have been developed to improve prescribing, but these have hardly ever been used in combination. The Systematic Tool to Reduce Inappropriate Prescribing (STRIP) combines implicit prescribing tools with the explicit Screening Tool to Alert physicians to the Right Treatment and Screening Tool of Older People's potentially inappropriate Prescriptions criteria and has shared decision-making with the patient as a critical step. This article describes the STRIP and its ability to identify potentially inappropriate prescribing. The STRIP improved general practitioners' and final-year medical students' medication review skills. The Web-application STRIP Assistant was developed to enable health care providers to use the STRIP in daily practice and will be incorporated in clinical decision support systems. It is currently being used in the European Optimizing thERapy to prevent Avoidable hospital admissions in the Multimorbid elderly (OPERAM) project, a multicentre randomized controlled trial involving patients aged 75 years and older using multiple medications for multiple medical conditions. In conclusion, the STRIP helps health care providers to systematically identify potentially inappropriate prescriptions and medication-related problems and to change the patient's medication regimen in accordance with the patient's needs and wishes. This article describes the STRIP and the available evidence so far. The OPERAM study is investigating the effect of STRIP use on clinical and economic outcomes

Risk identification-based association rule mining for supply chain big data

© Springer Nature Switzerland AG 2018. Since most supply chain processes include operational risks, the effectiveness of a corporation’s success depends mainly on identifying, analyzing and managing them. Currently, supply chain risk management (SCRM) is an active research field for enhancing a corporation’s efficiency. Although several techniques have been proposed, they still face a big challenge as they analyze only internal risk events from big data collected from the logistics of supply chain systems. In this paper, we analyze features that can identify risk labels in a supply chain. We propose defining risk events based on the association rule mining (ARM) technique that can categorize those in a supply chain based on a company’s historical data. The empirical results we obtained using data collected from an Aluminum company showed that this technique can efficiently generate and predict the optimal features of each risk label with a higher than 96.5% accuracy

A mixed methods analysis of the medication review intervention centered around the use of the Systematic Tool to Reduce Inappropriate Prescribing Assistant (STRIPA) in Swiss primary care practices

Background. Electronic clinical decision support systems (eCDSS), such as the ‘Systematic Tool to Reduce Inappropriate Prescribing’ Assistant (STRIPA), have become promising tools for assisting general practitioners (GPs) with conducting medication reviews in older adults. Little is known about how GPs perceive eCDSS-assisted recommendations for pharmacotherapy optimization. The aim of this study was to explore the implementation of a medication review intervention centered around STRIPA in the ‘Optimising PharmacoTherapy In the multimorbid elderly in primary CAre’ (OPTICA) trial. Methods. We used an explanatory mixed methods design combining quantitative and qualitative data. First, quantitative data about the acceptance and implementation of eCDSS-generated recommendations from GPs ( n = 21) and their patients ( n = 160) in the OPTICA intervention group were collected. Then, semi-structured qualitative interviews were conducted with GPs from the OPTICA intervention group ( n = 8), and interview data were analyzed through thematic analysis. Results. In quantitative findings, GPs reported averages of 13 min spent per patient preparing the eCDSS, 10 min performing medication reviews, and 5 min discussing prescribing recommendations with patients. On average, out of the mean generated 3.7 recommendations (SD=1.8). One recommendation to stop or start a medication was reported to be implemented per patient in the intervention group (SD=1.2). Overall, GPs found the STRIPA useful and acceptable. They particularly appreciated its ability to generate recommendations based on large amounts of patient information. During qualitative interviews, GPs reported the main reasons for limited implementation of STRIPA were related to problems with data sourcing (e.g., incomplete data imports), preparation of the eCDSS (e.g., time expenditure for updating and adapting information), its functionality (e.g., technical problems downloading PDF recommendation reports), and appropriateness of recommendations. Conclusions. Qualitative findings help explain the relatively low implementation of recommendations demonstrated by quantitative findings, but also show GPs’ overall acceptance of STRIPA. Our results provide crucial insights for adapting STRIPA to make it more suitable for regular use in future primary care settings (e.g., necessity to improve data imports). Trial registration. Clinicaltrials.gov NCT03724539, date of first registration: 29/10/2018</p