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Background: Bedside clinical pharmacy prevents drug-related problems, but is not feasible in many countries due to limited resources. Hence, clinical rules using structural information in the electronic health record can help identifying potentially inappropriate prescriptions (PIPs). We aimed to develop and implement a risk-based clinical pharmacy service and evaluate its impact on prescribing at the trauma surgery ward. Methods: The proportion of residual PIPs per day, i.e. the number of PIPs that persisted up to 24 h after pharmacist intervention divided by the number of PIPs at T0, was evaluated before and after implementation of the intervention in an interrupted time series analysis. The pre-intervention cohort received usual pharmacy services, i.e. a 0.3 FTE clinical pharmacist trainee. Fifteen clinical rules, targeting antimicrobial, anticoagulant and analgesic therapy were implemented in the post-intervention period. The pre-intervention period was compared to two post-intervention scenarios: A) clinical rule alerts reviewed by a 0.3 FTE clinical pharmacist trainee; and B) clinical rule alerts reviewed daily for approximately 1 h by a clinical pharmacist trainee. Results: Pre-intervention, a median proportion of 67% (range 0%−100%) residual PIPs per day was observed. Scenario A showed an immediate relative reduction of 14% (p = 0.72) and scenario B a significant immediate relative reduction of 85% (p = 0.0015) in residual PIPs per day. In scenario A, recommendations were provided for 19% of clinical rule alerts, of which 67% was accepted by the surgeon within 24 h. In scenario B, recommendations were given for 56% of alerts, of which 84% was accepted. Conclusions: Using clinical rules is an effective approach to organize bedside clinical pharmacy services and improves prescribing at the trauma surgery ward. Advanced training and daily follow-up of the clinical rules are two requirements to be considered.

Abstract Background Adverse drug events (ADEs) can be prevented by deploying clinical decision support systems (CDSS) that directly assist physicians, via computerized order entry systems, and clinical pharmacists performing medication reviews as part of medical rounds. However, physicians using CDSS are known to be exposed to the alert-fatigue phenomenon. Our study aimed to assess the performance of PharmaCheck—a CDSS to help clinical pharmacists detect high-risk situations with the potential to lead to ADEs—and its impact on clinical pharmacists’ activities. Methods Twenty clinical rules, divided into four risk classes, were set for the daily screening of high-risk situations in the electronic health records of patients admitted to our General Internal Medicine Department. Alerts to clinical pharmacists encouraged them to telephone prescribers and suggest any necessary treatment adjustments. PharmaCheck’s performance was assessed using the intervention’s positive predictive value (PPV), which characterizes the proportion of interventions for each alert triggered. PharmaCheck’s impact was assessed by considering clinical pharmacists as a filter for ruling out futile alerts and by comparing the final clinical PPV with a pharmacist (the proportion of interventions that led to a change in the medical regimen) to the final clinical PPV without a pharmacist. Results Over 132 days, 447 alerts were triggered for 383 patients, leading to 90 interventions (overall intervention PPV = 20.1%). By risk class, intervention PPVs made up 26.9% (n = 65/242) of abnormal laboratory value alerts, 3.1% (4/127) of alerts for contraindicated medications or medications to be used with caution, 28.2% (20/71) of drug–drug interaction alerts, and 14.3% (1/7) of inadequate mode of administration alerts. Clinical PPVs reached 71.0% (64/90) when pharmacists filtered alerts and 14% (64/242) if they were not doing it. Conclusion PharmaCheck enabled clinical pharmacists to improve their traditional processes and broaden their coverage by focusing on 20 high-risk situations. Alert management by pharmacists seemed to be a more effective way of preventing risky situations and alert-fatigue than a model addressing alerts to physicians exclusively. Some fine-tuning could enhance PharmaCheck's performance by considering the information quality of triggers, the variability of clinical settings, and the fact that some prescription processes are already highly secured.

Abstract Background For emergency department (ED) patients with suspected infection, a vital sign-based clinical rule is often calculated shortly after the patient arrives. The clinical rule score (normal or abnormal) provides information about diagnosis and/or prognosis. Since vital signs vary over time, the clinical rule scores can change as well. In this prospective multicentre study, we investigate how often the scores of four frequently used clinical rules change during the ED stay of patients with suspected infection. Methods Adult (≥ 18 years) patients with suspected infection were prospectively included in three Dutch EDs between March 2016 and December 2019. Vital signs were measured in 30-min intervals and the quick Sequential Organ Failure Assessment (qSOFA) score, the Systemic Inflammatory Response Syndrome (SIRS) criteria, the Modified Early Warning Score and the National Early Warning Score (NEWS) score were calculated. Using the established cut-off points, we analysed how often alterations in clinical rule scores occurred (i.e. switched from normal to abnormal or vice versa). In addition, we investigated which vital signs caused most alterations. Results We included 1433 patients, of whom a clinical rule score changed once or more in 637 (44.5%) patients. In 6.7–17.5% (depending on the clinical rule) of patients with an initial negative clinical rule score, a positive score occurred later during ED stay. In over half (54.3–65.0%) of patients with an initial positive clinical rule score, the score became negative later on. The respiratory rate caused most (51.2%) alterations. Conclusion After ED arrival, alterations in qSOFA, SIRS, MEWS and/or NEWS score are present in almost half of patients with suspected infection. The most contributing vital sign to these alterations was the respiratory rate. One in 6–15 patients displayed an abnormal clinical rule score after a normal initial score. Clinicians should be aware of the frequency of these alterations in clinical rule scores, as clinical rules are widely used for diagnosis and/or prognosis and the optimal moment of assessing them is unknown.

Background: Adverse drug events (ADEs) can be prevented by deploying clinical decision support systems (CDSS) that directly assist physicians, via computerized order entry systems, and clinical pharmacists performing medication reviews as part of medical rounds. However, physicians using CDSS are known to be exposed to the alert-fatigue phenomenon. Our study aimed to assess the performance of PharmaCheck-a CDSS to help clinical pharmacists detect high-risk situations with the potential to lead to ADEs-and its impact on clinical pharmacists' activities.Methods: Twenty clinical rules, divided into four risk classes, were set for the daily screening of high-risk situations in the electronic health records of patients admitted to our General Internal Medicine Department. Alerts to clinical pharmacists encouraged them to telephone prescribers and suggest any necessary treatment adjustments. PharmaCheck's performance was assessed using the intervention's positive predictive value (PPV), which characterizes the proportion of interventions for each alert triggered. PharmaCheck's impact was assessed by considering clinical pharmacists as a filter for ruling out futile alerts and by comparing the final clinical PPV with a pharmacist (the proportion of interventions that led to a change in the medical regimen) to the final clinical PPV without a pharmacist.Results: Over 132 days, 447 alerts were triggered for 383 patients, leading to 90 interventions (overall intervention PPV = 20.1%). By risk class, intervention PPVs made up 26.9% (n = 65/242) of abnormal laboratory value alerts, 3.1% (4/127) of alerts for contraindicated medications or medications to be used with caution, 28.2% (20/71) of drug-drug interaction alerts, and 14.3% (1/7) of inadequate mode of administration alerts. Clinical PPVs reached 71.0% (64/90) when pharmacists filtered alerts and 14% (64/242) if they were not doing it.Conclusion: PharmaCheck enabled clinical pharmacists to improve their traditional processes and broaden their coverage by focusing on 20 high-risk situations. Alert management by pharmacists seemed to be a more effective way of preventing risky situations and alert-fatigue than a model addressing alerts to physicians exclusively. Some fine-tuning could enhance PharmaCheck's performance by considering the information quality of triggers, the variability of clinical settings, and the fact that some prescription processes are already highly secured. [ABSTRACT FROM AUTHOR]

Background: For emergency department (ED) patients with suspected infection, a vital sign-based clinical rule is often calculated shortly after the patient arrives. The clinical rule score (normal or abnormal) provides information about diagnosis and/or prognosis. Since vital signs vary over time, the clinical rule scores can change as well. In this prospective multicentre study, we investigate how often the scores of four frequently used clinical rules change during the ED stay of patients with suspected infection. Methods: Adult (≥ 18 years) patients with suspected infection were prospectively included in three Dutch EDs between March 2016 and December 2019. Vital signs were measured in 30-min intervals and the quick Sequential Organ Failure Assessment (qSOFA) score, the Systemic Inflammatory Response Syndrome (SIRS) criteria, the Modified Early Warning Score and the National Early Warning Score (NEWS) score were calculated. Using the established cut-off points, we analysed how often alterations in clinical rule scores occurred (i.e. switched from normal to abnormal or vice versa). In addition, we investigated which vital signs caused most alterations. Results: We included 1433 patients, of whom a clinical rule score changed once or more in 637 (44.5%) patients. In 6.7–17.5% (depending on the clinical rule) of patients with an initial negative clinical rule score, a positive score occurred later during ED stay. In over half (54.3–65.0%) of patients with an initial positive clinical rule score, the score became negative later on. The respiratory rate caused most (51.2%) alterations. Conclusion: After ED arrival, alterations in qSOFA, SIRS, MEWS and/or NEWS score are present in almost half of patients with suspected infection. The most contributing vital sign to these alterations was the respiratory rate. One in 6–15 patients displayed an abnormal clinical rule score after a normal initial score. Clinicians should be aware of the frequency of these alterations in clinical rule scores, as clinical rules are widely used for diagnosis and/or prognosis and the optimal moment of assessing them is unknown. [ABSTRACT FROM AUTHOR]

Abstract Background To improve medication surveillance and provide pharmacotherapeutic support in University Hospitals Leuven, a back-office clinical service, called “Check of Medication Appropriateness” (CMA), was developed, consisting of clinical rule based screening for medication inappropriateness. The aim of this study is twofold: 1) describing the development of CMA and 2) evaluating the preliminary results, more specifically the number of clinical rule alerts, number of actions on the alerts and acceptance rate by physicians. Methods CMA focuses on patients at risk for potentially inappropriate medication and involves the daily checking by a pharmacist of high-risk prescriptions generated by advanced clinical rules integrating patient specific characteristics with details on medication. Pharmacists’ actions are performed by adding an electronic note in the patients’ medical record or by contacting the physician by phone. A retrospective observational study was performed to evaluate the primary outcomes during an 18-month study period. Results 39,481 clinical rule alerts were checked by pharmacists for which 2568 (7%) electronic notes were sent and 637 (1.6%) phone calls were performed. 37,782 (96%) alerts were checked within four pharmacotherapeutic categories: drug use in renal insufficiency (25%), QTc interval prolonging drugs (11%), drugs with a restricted indication or dosing (14%) and overruled very severe drug-drug interactions (50%). The emergency department was a frequently involved ward and anticoagulants are the drug class for which actions are most frequently carried out. From the 458 actions performed for the four abovementioned categories, 69% were accepted by physicians. Conclusions These results demonstrate the added value of CMA to support medication surveillance in synergy with already integrated basic clinical decision support and bedside clinical pharmacy. Otherwise, the study also highlighted a number of limitations, allowing improvement of the service.

Background: Bedside clinical pharmacy prevents drug-related problems, but is not feasible in many countries due to limited resources. Hence, clinical rules using structural information in the electronic health record can help identifying potentially inappropriate prescriptions (PIPs). We aimed to develop and implement a risk-based clinical pharmacy service and evaluate its impact on prescribing at the trauma surgery ward., Methods: The proportion of residual PIPs per day, i.e. the number of PIPs that persisted up to 24 h after pharmacist intervention divided by the number of PIPs at T0, was evaluated before and after implementation of the intervention in an interrupted time series analysis. The pre-intervention cohort received usual pharmacy services, i.e. a 0.3 FTE clinical pharmacist trainee. Fifteen clinical rules, targeting antimicrobial, anticoagulant and analgesic therapy were implemented in the post-intervention period. The pre-intervention period was compared to two post-intervention scenarios: A) clinical rule alerts reviewed by a 0.3 FTE clinical pharmacist trainee; and B) clinical rule alerts reviewed daily for approximately 1 h by a clinical pharmacist trainee., Results: Pre-intervention, a median proportion of 67% (range 0%-100%) residual PIPs per day was observed. Scenario A showed an immediate relative reduction of 14% (p = 0.72) and scenario B a significant immediate relative reduction of 85% (p = 0.0015) in residual PIPs per day. In scenario A, recommendations were provided for 19% of clinical rule alerts, of which 67% was accepted by the surgeon within 24 h. In scenario B, recommendations were given for 56% of alerts, of which 84% was accepted., Conclusions: Using clinical rules is an effective approach to organize bedside clinical pharmacy services and improves prescribing at the trauma surgery ward. Advanced training and daily follow-up of the clinical rules are two requirements to be considered., Competing Interests: All authors declare that they have no conflict of interest related to this work., (© 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

[Display omitted] • This paper reviews clinical decision system architectures utilizing a combination of rule-based reasoning and machine learning to identify recurrent architecture archetypes and motivations of their authors. • Five distinct architecture archetypes were identified: Rules are Embedded in ML architecture (REML), ML pre-processes input data for Rule-Based inference (MLRB), Rule-Based method pre-processes input data for ML prediction (RBML), Rules influence ML training (RMLT), Parallel Ensemble of Rules and ML (PERML). • Parallel ensembles that leverage the benefits of machine learning and rule-based reasoning are rare in the present literature and offer a promising avenue for further research and exploration. As the application of Artificial Intelligence (AI) technologies increases in the healthcare sector, the industry faces a need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML), which offer high prediction accuracy at the expense of transparency of decision making. This paper seeks to review the present literature, identify hybrid architecture patterns that incorporate rules and machine learning, and evaluate the rationale behind their selection to inform future development and research on the design of transparent and precise clinical decision systems. PubMed, IEEE Explore, and Google Scholar were queried in search for papers from 1992 to 2022, with the keywords: "clinical decision system", "hybrid clinical architecture", "machine learning and clinical rules". Excluded articles did not use both ML and rules or did not provide any explanation of employed architecture. A proposed taxonomy was used to organize the results, analyze them, and depict them in graphical and tabular form. Two researchers, one with expertise in rule-based systems and another in ML, reviewed identified papers and discussed the work to minimize bias, and the third one re-reviewed the work to ensure consistency of reporting. The authors screened 957 papers and reviewed 71 that met their criteria. Five distinct architecture archetypes were determined: Rules are Embedded in ML architecture (REML) (most used), ML pre-processes input data for Rule-Based inference (MLRB), Rule-Based method pre-processes input data for ML prediction (RBML), Rules influence ML training (RMLT), Parallel Ensemble of Rules and ML (PERML), which was rarely observed in clinical contexts. Most architectures in the reviewed literature prioritize prediction accuracy over explainability and trustworthiness, which has led to more complex embedded approaches. Alternatively, parallel (PERML) architectures may be employed, allowing for a more transparent system that is easier to explain to patients and clinicians. The potential of this approach warrants further research. A limitation of the study may be that it reviews scientific literature, while algorithms implemented in clinical practice may present different distributions of motivations and implementations of hybrid architectures. [ABSTRACT FROM AUTHOR]

Purpose: To assess the extent to which clinical rules (CRs) can be implemented for automatic evaluation of quality of care in general practice.Methods: We assessed 81 clinical rules (CRs) adapted from a subset of Assessing Care of Vulnerable Elders (ACOVE) clinical rules, against Dutch College of General Practitioners (NHG) data model. Each CR was analyzed using the Logical Elements Rule METHOD: (LERM). LERM is a stepwise method of assessing and formalizing clinical rules for decision support. Clinical rules that satisfied the criteria outlined in the LERM method were judged to be implementable in automatic evaluation in general practice.Results: Thirty-three out of 81 (40.7%) Dutch-translated ACOVE clinical rules can be automatically evaluated in electronic medical record systems. Seven out of 7 CRs (100%) in the domain of diabetes can be automatically evaluated, 9/17 (52.9%) in medication use, 5/10 (50%) in depression care, 3/6 (50%) in nutrition care, 6/13 (46.1%) in dementia care, 1/6 (16.6%) in end of life care, 2/13 (15.3%) in continuity of care, and 0/9 (0%) in the fall-related care. Lack of documentation of care activities between primary and secondary health facilities and ambiguous formulation of clinical rules were the main reasons for the inability to automate the clinical rules.Conclusion: Approximately two-fifths of the primary care Dutch ACOVE-based clinical rules can be automatically evaluated. Clear definition of clinical rules, improved GP database design and electronic linkage of primary and secondary healthcare facilities can improve prospects of automatic assessment of quality of care. These findings are relevant especially because the Netherlands has very high automation of primary care. [ABSTRACT FROM AUTHOR]

Background: Patients with testicular germ cell cancer (GCC) have a high cure rate; however, cancer-related fatigue is the most common complication among patients with GCC undergoing treatment with chemotherapy. Although exercise is widely recommended, information about the physio-pathological effects of cancer therapy on skeletal muscle is very limited. Our aim is to evaluate the effects of an individualized program of muscular strength and endurance with aerobic training on cancer-related fatigue.Methods/design: The present study is a randomized controlled trial comparing an individualized program of muscular strength and endurance with aerobic training compared to a control group. We will conduct this trial in patients undergoing chemotherapy, recruited by the Department of Oncology of Virgen de la Victoria Hospital (Málaga). Patients will be included and evaluated before the first cycle of chemotherapy and assigned randomly to the experimental or control group. Cancer-related fatigue, physical condition and biological samples will be measured at the beginning and at the end of an 8-week intervention by the same evaluator, who will be unaware of the allocation of participants to each group. Furthermore, there will be monitoring for 6 months (24 weeks) after training for all outcome variables.Discussion: This study hopes to offer patients with GCC an individualized exercise program with aerobic training for cancer-related fatigue. Such a scheme, if beneficial, could be implemented successfully within public health.Trial Registration: ClinicalTrials.gov Identifier: NCT02433197 . Date of registration: 13 April 2015. [ABSTRACT FROM AUTHOR]

Clinical Decision Support Systems have been shown to increase quality of care, patient safety, improve adherence to guidelines for prevention and treatment, and avoid medication errors. Such systems depend mainly on two types of content; the clinical information related to patients and the medical knowledge related to the specialty that informs the system rules and alerts. At King Faisal Specialist Hospital and Research Center, Saudi Arabia, the Health Information Technology Affairs worked on identifying best strategies and recommendations for successful CDSS knowledge management. A review of literature was conducted to identify main areas of challenges and factors of success. A qualitative survey was used over six months' duration to collect opinions, experiences and suggestions from both IT and healthcare professionals. Recommendations were categorized into ten main topics that should be addressed during the development and implementation of CDSS knowledge management tools in the hospital. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: Our advanced clinical decision support (CDS) system, entitled ‘adverse drug event alerting system’ (ADEAS), is in daily use in our hospital pharmacy. It is used by hospital pharmacists to select patients at risk of possible adverse drug events (ADEs). The system retrieves data from several information systems, and uses clinical rules to select the patients at risk of ADEs. The clinical rules are all medication related and are formulated using seven risk categories. Objective: This studies objectives are to 1) evaluate the use of the CDS system ADEAS in daily hospital pharmacy practice, and 2) assess the rule effectiveness and positive predictive value (PPV) of the clinical rules incorporated in the system. Setting: Leiden University Medical Center, The Netherlands. All patients admitted on six different internal medicine and cardiology wards were included. Measures: Outcome measures were total number of alerts, number of patients with alerts and the outcome of these alerts: whether the hospital pharmacist gave advice to prevent a possible ADE or not. Both overall rule effectiveness and PPV and rule effectiveness and PPV per clinical rule risk category were scored. Study design: During a 5 month study period safety alerts were generated daily by means of ADEAS. All alerts were evaluated by a hospital pharmacist and if necessary, healthcare professionals were subsequently contacted and advice was given in order to prevent possible ADEs. Results: During the study period ADEAS generated 2650 safety alerts in 931 patients. In 270 alerts (10%) the hospital pharmacist contacted the physician or nurse and in 204 (76%) cases this led to an advice to prevent a possible ADE. The remaining 2380 alerts (90%) were scored as non-relevant. Most alerts were generated with clinical rules linking pharmacy and laboratory data (1685 alerts). The overall rule effectiveness was 0.10 and the overall PPV was 0.08. Combination of rule effectiveness and PPV was highest for clinical rules based upon the risk category “basic computerized physician order entry (CPOE) medication safety alerts fine-tuned to high risk patients” (rule efficiency=0.17; PPV=0.14). Conclusion: ADEAS can effectively be used in daily hospital pharmacy practice to select patients at risk of potential ADEs, but to increase the benefits for routine patient care and to increase efficiency, both rule effectiveness and PPV for the clinical rules should be improved. Furthermore, clinical rules would have to be refined and restricted to those categories that are potentially most promising for clinical relevance, i.e. “clinical rules with a combination of pharmacy and laboratory data” and “clinical rules based upon the basic CPOE medication safety alerts fine-tuned to high risk patients”. [Copyright &y& Elsevier]

Abstract: Background: In intensive care environments, technology is omnipresent whereby ensuring constant monitoring and the administration of critical drugs to unstable patients. A clinical decision support system (CDSS), with its widespread possibilities, can be a valuable tool in supporting adequate patient care. However, it is still unclear how decision support alerts should be presented to physicians and other medical staff to ensure that they are used most effectively. Objective: To determine the effect of four different alert presentation methods on alert compliance after the implementation of an advanced CDSS on the intensive care unit (ICU) in our hospital. Methods: A randomized clinical trial was executed from August 2010 till December 2011, which included all patients admitted to the ICU of our hospital. The CDSS applied contained a set of thirteen locally developed clinical rules. The percentage of alert compliance was compared for four alert presentation methods: pharmacy intervention, physician alert list, electronic health record (EHR) section and pop-up alerts. Additionally, surveys were held to determine the method most preferred by users of the CDSS. Results: In the study period, the CDSS generated 902 unique alerts, primarily due to drug dosing during decreased renal function and potassium disturbances. Alert compliance was highest for recommendations offered in pop-up alerts (41%, n =68/166), followed by pharmacy intervention (33%, n =80/244), the physician alert list (20%, n =40/199) and the EHR section (19%, n =55/293). The method most preferred by clinicians was pharmacy intervention, and pop-up alerts were found suitable as well if applied correctly. The physician alert list and EHR section were not considered suitable for CDSSs in the process of this study. Conclusion: The alert presentation method used for CDSSs is crucial for the compliance with alerts for the clinical rules and, consequently, for the efficacy of these systems. Active alerts such as pop-ups and pharmacy intervention were more effective than passive alerts, which do not automatically appear within the clinical workflow. In this pilot study, ICU clinicians also preferred pharmacy intervention and pop-up alerts. More research is required to expand these results to other departments and other hospitals, as well as to other types of CDSSs and different alert presentation methods. [Copyright &y& Elsevier]

The Logical Elements Rule Method (LERM) is a step-wise method for formalizing if-then clinical rules. We applied LERM to a set of 40 clinical rules used in pharmacological quality assessment initiatives to assess (1) the amenability of the rules to formalization for decision support application (2) comparing adherence to rules that can and cannot be formalized, and (3) the usefulness of LERM as a tool for this task. Five rules could not be formalized, all due to unclear decision criteria. The adherence to ambiguous, non-formalizable rules was significantly lower than for formalizable ones (<0.001). We modified LERM with three additions for this task: (a) adding the sub-step of restating the rules in a consistent natural-language grammar before decomposing them into normal form, (b) creating rules to use in lieu of a controlled vocabulary, and (c) adding the requirement that a time frame must be defined for all medications (before hospitalization, current medication, new medication, or discharge medication). Although the clinical rules in this sample are all stated as semi-structured if-then recommendations and are used in quality assessment initiatives, many ambiguities and inconsistencies in the clinical rules were identified by using LERM. [ABSTRACT FROM AUTHOR]

This paper describes the deployment of a platform to enable processing of currently uncharted high frequency, high fidelity, synchronous data from medical devices. Such a platform would support the next generation of informatics solutions for neonatal intensive care. We present Artemis, a platform for real-time enactment of clinical knowledge as it relates to multidimensional data analysis and clinical research. Through specific deployment examples at two different neonatal intensive care units, we demonstrate that Artemis supports: 1) instantiation of clinical rules; 2) multidimensional analysis; 3) distribution of services for critical care via cloud computing; and 4) accomplishing 1 through 3 using current technology without a negative impact on patient care. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: The objectives of the study were as follows: (1) to investigate whether the predictive validity of a previously developed back pain prediction rule could be improved; (2) to determine if the rule can be shortened without loss of predictive validity; (3) to compare the rule with the physician''s judgment; (4) to assess, in a different population, its 2-year predictive validity; and (5) to evaluate the clinical applicability of the rule in a first-line care setting. Study Design and Setting: One thousand two hundred and sixty-two participants were enrolled in the study (participation: 91%) before a medical consultation for nonspecific back pain in a large emergency room and were followed up for 2 years (follow-up: 92.5%). The effects of adding new items and deleting any one of the original items were evaluated. The predictions by the rule and the physicians were compared with the 2-year actual functional limitations (measured with the Roland–Morris Disability Questionnaire). Results: Although the final prediction rule included only five items (feeling everything is an effort, trouble getting breath, hot/cold spells, numbness/tingling in parts of body, and pain in heart/chest), its predictive validity was greater than that of the original 17-item version and was superior to the physician''s prediction. The rule was easily applied. Conclusion: A five-item clinical prediction rule of long-term back-related functional limitations could help first-line care physicians to concentrate the clinical attention on patients at higher risk. [Copyright &y& Elsevier]

Clinical decision support systems (CDSS) are the new generation clinical support tools that ‘make it easy to do it right’. Despite promising results, these systems are not common practice, although experts agree that the necessary revolution in health care will depend on its implementation. To accelerate adoption a strategy is handed for structured development and validation of CDSS' content (clinical rules). The first results show that the proposed strategy is easily applicable for creating specific and reliable rules, generating relevant recommendations. [ABSTRACT FROM AUTHOR]

Background: To improve medication surveillance and provide pharmacotherapeutic support in University Hospitals Leuven, a back-office clinical service, called "Check of Medication Appropriateness" (CMA), was developed, consisting of clinical rule based screening for medication inappropriateness. The aim of this study is twofold: 1) describing the development of CMA and 2) evaluating the preliminary results, more specifically the number of clinical rule alerts, number of actions on the alerts and acceptance rate by physicians.Methods: CMA focuses on patients at risk for potentially inappropriate medication and involves the daily checking by a pharmacist of high-risk prescriptions generated by advanced clinical rules integrating patient specific characteristics with details on medication. Pharmacists' actions are performed by adding an electronic note in the patients' medical record or by contacting the physician by phone. A retrospective observational study was performed to evaluate the primary outcomes during an 18-month study period.Results: 39,481 clinical rule alerts were checked by pharmacists for which 2568 (7%) electronic notes were sent and 637 (1.6%) phone calls were performed. 37,782 (96%) alerts were checked within four pharmacotherapeutic categories: drug use in renal insufficiency (25%), QTc interval prolonging drugs (11%), drugs with a restricted indication or dosing (14%) and overruled very severe drug-drug interactions (50%). The emergency department was a frequently involved ward and anticoagulants are the drug class for which actions are most frequently carried out. From the 458 actions performed for the four abovementioned categories, 69% were accepted by physicians.Conclusions: These results demonstrate the added value of CMA to support medication surveillance in synergy with already integrated basic clinical decision support and bedside clinical pharmacy. Otherwise, the study also highlighted a number of limitations, allowing improvement of the service. [ABSTRACT FROM AUTHOR]

Introduction Adverse drug events (ADEs) are frequent and pose an important risk for patients treated with drugs. Fortunately, a substantial part of ADEs is preventable, and computerised physician order entry with a sophisticated clinical decision support system may be used to reach this goal. Objective To develop a new automated system that could improve the quality of medication surveillance. The system should focus on detecting patients at risk for an ADE by combining data from the hospital information system and computerised physician order entry (drug prescription data, drug–drug interaction alerts, clinical chemical laboratory parameters, demographic features), using clinical rules. Methods The clinical rules were formulated in a multidisciplinary team, based on seven risk categories. The new system was composed in a guideline-based decision support framework consisting of both a guideline development module and a decision support module. A total of 121 clinical rules were built into the system. Validation of the system and a proof of principle test were performed. Results The adverse drug event alerting system (ADEAS) was developed and validated successfully. The proof of principle test showed that ADEAS has potential clinical usefulness. ADEAS generated alerts and detected additional potential risk situations, which were not generated by the conventional medication surveillance. Conclusion We developed a pharmacy decision support system ADEAS that focusses on the detection of situations prone to lead to an ADE and might help clinicians to take timely corrective interventions and thereby can prevent patient harm. [ABSTRACT FROM PUBLISHER]