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Objective Combining text mining (TM) and clinical decision support (CDS) could improve diagnostic and therapeutic processes in clinical practice. This review summarizes current knowledge of the TM-CDS combination in clinical practice, including their intended purpose, implementation in clinical practice, and barriers to such implementation. Materials and Methods A search was conducted in PubMed, EMBASE, and Cochrane Library databases to identify full-text English language studies published before January 2022 with TM-CDS combination in clinical practice. Results Of 714 identified and screened unique publications, 39 were included. The majority of the included studies are related to diagnosis (n  =   26) or prognosis (n  =   11) and used a method that was developed for a specific clinical domain, document type, or application. Most of the studies selected text containing parts of the electronic health record (EHR), such as reports (41%, n  =   16) and free-text narratives (36%, n  =   14), and 23 studies utilized a tool that had software "developed for the study". In 15 studies, the software source was openly available. In 79% of studies, the tool was not implemented in clinical practice. Barriers to implement these tools included the complexity of natural language, EHR incompleteness, validation and performance of the tool, lack of input from an expert team, and the adoption rate among professionals. Discussion/Conclusions The available evidence indicates that the TM-CDS combination may improve diagnostic and therapeutic processes, contributing to increased patient safety. However, further research is needed to identify barriers to implementation and the impact of such tools in clinical practice. [ABSTRACT FROM AUTHOR]

What is known and objective: Physicians' response to moderate and severe hypokalaemia in hospitalized patients is frequently suboptimal, leading to increased risk of cardiac arrhythmias and sudden death. While actively alerting physicians on all critical care values using telephone or electronic pop‐ups can improve response, it can also lead to alert fatigue and frustration due to non‐specific and overdue alerts. Therefore, a new method was tested. A clinical rule built into a clinical decision support system (CDSS) generated alerts for patients with a serum potassium level (SPL) <2.9 mmol/L without a prescription for potassium supplementation. If the alert was deemed clinically relevant, a pharmacist contacted the physician. The aim of this study was to evaluate the impact of the clinical rule‐guided pharmacists' intervention compared to showing passive alerts in the electronic health records on outcome in patients who developed hypokalaemia (<2.9 mmol/L) during hospitalization. Methods: A before (2007‐2009) and after (2010‐2017) study with time series design was performed. Pre‐intervention, physicians were shown passive alerts for hypokalaemia in the electronic health records. During the intervention period, in addition to these passive alerts, a pharmacist provided the physician with a specific advice on patients with untreated hypokalaemia, guided by the generated alerts. Unique patients >18 years with SPL <2.9 mmol/L measured at least 24 hours after hospitalization in whom no potassium supplementation was initiated within 4 hours after measurement and normalization of SPL was not achieved within these 4 hours were included. Haemodialysis patients were excluded. The percentage of hypokalaemic patients with a subsequent prescription for potassium supplementation, time to subsequent potassium supplementation prescription, the percentage of patients who achieved normokalaemia (SPL ≥ 3.0 mmol/L), time to achieve normokalaemia and total duration of hospitalization were compared. Results and discussion: A total of 693 patients were included, of whom 278 participated in the intervention phase. The percentage of patients prescribed supplementation as well as time to prescription improved from 76.0% in 31.1 hours to 92.0% in 11.3 hours (P <.01). Time to achieve SPL ≥3.0 mmol/L improved, P <.009. No changes, however, were observed in the percentage of patients who achieved normokalaemia or time to reach normokalaemia, 87.5% in 65.2 hours pre‐intervention compared to 90.2% (P =.69) in 64.0 hours (P =.71) in the intervention group. A non‐significant decrease of 8.2 days was observed in the duration of hospitalization: 25.4 compared to 17.2 days (P =.29). What is new and conclusion: Combining CDSS alerting with a pharmacist evaluation is an effective method to improve response rate, time to supplementation and time to initial improvement, defined as SPL ≥3.0 mmol/L. However, it showed no significant effect on the percentage of patients achieving normokalaemia, time to normokalaemia or hospitalization. The discrepancy between rapid supplementation and improvement on the one hand and failure to improve time to normokalaemia on the other warrants further study. [ABSTRACT FROM AUTHOR]

Objective: Adverse drug reactions (ADRs) are a significant healthcare concern. They are often documented as free text in electronic health records (EHRs), making them challenging to use in clinical decision support systems (CDSS). The study aimed to develop a text mining algorithm to identify ADRs in free text of Dutch EHRs., Materials and Methods: In Phase I, our previously developed CDSS algorithm was recoded and improved upon with the same relatively large dataset of 35 000 notes (Step A), using R to identify possible ADRs with Medical Dictionary for Regulatory Activities (MedDRA) terms and the related Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) (Step B). In Phase II, 6 existing text-mining R-scripts were used to detect and present unique ADRs, and positive predictive value (PPV) and sensitivity were observed., Results: In Phase IA, the recoded algorithm performed better than the previously developed CDSS algorithm, resulting in a PPV of 13% and a sensitivity of 93%. For The sensitivity for serious ADRs was 95%. The algorithm identified 58 additional possible ADRs. In Phase IB, the algorithm achieved a PPV of 10%, a sensitivity of 86%, and an F-measure of 0.18. In Phase II, four R-scripts enhanced the sensitivity and PPV of the algorithm, resulting in a PPV of 70%, a sensitivity of 73%, an F-measure of 0.71, and a 63% sensitivity for serious ADRs., Discussion and Conclusion: The recoded Dutch algorithm effectively identifies ADRs from free-text Dutch EHRs using R-scripts and MedDRA/SNOMED-CT. The study details its limitations, highlighting the algorithm's potential and significant improvements., Competing Interests: The authors have no competing interests to declare., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: Combining text mining (TM) and clinical decision support (CDS) could improve diagnostic and therapeutic processes in clinical practice. This review summarizes current knowledge of the TM-CDS combination in clinical practice, including their intended purpose, implementation in clinical practice, and barriers to such implementation., Materials and Methods: A search was conducted in PubMed, EMBASE, and Cochrane Library databases to identify full-text English language studies published before January 2022 with TM-CDS combination in clinical practice., Results: Of 714 identified and screened unique publications, 39 were included. The majority of the included studies are related to diagnosis (n = 26) or prognosis (n = 11) and used a method that was developed for a specific clinical domain, document type, or application. Most of the studies selected text containing parts of the electronic health record (EHR), such as reports (41%, n = 16) and free-text narratives (36%, n = 14), and 23 studies utilized a tool that had software "developed for the study". In 15 studies, the software source was openly available. In 79% of studies, the tool was not implemented in clinical practice. Barriers to implement these tools included the complexity of natural language, EHR incompleteness, validation and performance of the tool, lack of input from an expert team, and the adoption rate among professionals., Discussion/conclusions: The available evidence indicates that the TM-CDS combination may improve diagnostic and therapeutic processes, contributing to increased patient safety. However, further research is needed to identify barriers to implementation and the impact of such tools in clinical practice., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Drug-drug interactions (DDIs) frequently trigger adverse drug events or reduced efficacy. Most DDI alerts, however, are overridden because of irrelevance for the specific patient. Basic DDI clinical decision support (CDS) systems offer limited possibilities for decreasing the number of irrelevant DDI alerts without missing relevant ones. Computerized decision tree rules were designed to context-dependently suppress irrelevant DDI alerts. A crossover study was performed to compare the clinical utility of contextualized and basic DDI management in hospitalized patients. First, a basic DDI-CDS system was used in clinical practice while contextualized DDI alerts were collected in the background. Next, this process was reversed. All medication orders (MOs) from hospitalized patients with at least one DDI alert were included. The following outcome measures were used to assess clinical utility: positive predictive value (PPV), negative predictive value (NPV), number of pharmacy interventions (PIs)/1,000 MOs, and the median time spent on DDI management/1,000 MOs. During the basic DDI management phase 1,919 MOs/day were included, triggering 220 DDI alerts/1,000 MOs; showing 57 basic DDI alerts/1,000 MOs to pharmacy staff; PPV was 2.8% with 1.6 PIs/1,000 MOs costing 37.2 minutes/1,000 MOs. No DDIs were missed by the contextualized CDS system (NPV 100%). During the contextualized DDI management phase 1,853 MOs/day were included, triggering 244 basic DDI alerts/1,000 MOs, showing 9.6 contextualized DDIs/1,000 MOs to pharmacy staff; PPV was 41.4% (P < 0.01), with 4.0 PIs/1,000 MOs (P < 0.01) and 13.7 minutes/1,000 MOs. The clinical utility of contextualized DDI management exceeds that of basic DDI management., (© 2022 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Background: Administering medication through an enteral feeding tube (FT) is a frequent cause of errors resulting in increased morbidity and cost. Studies on interventions to prevent these errors in hospitalized patients, however, are limited., Objective: The objective was to study the effect of a clinical decision support system (CDSS)-assisted pharmacy intervention on the incidence of FT-related medication errors (FTRMEs) in hospitalized patients., Methods: A pre-post intervention study was conducted between October 2014 and May 2015 in Catharina Hospital, the Netherlands. Patients who were admitted to the wards of bowel and liver disease, oncology, or neurology; using oral medication; and had an enteral FT were included. Preintervention patients were given care as usual. The intervention consisted of implementing a CDSS-assisted pharmacy check while also implementing standard operating procedures and educating personnel. An FTRME was defined as the administration of inappropriate medication through an enteral FT. The incidence was expressed as the number of FTRMEs per medication administration. Multivariate Poisson regression was used to calculate the incidence ratio (IR) comparing both phases., Results: Eighty-one patients were included, 38 during preintervention and 43 during the intervention phase. Incidence of FTRMEs in the preintervention phase was 0.15 (95% CI, 0.07-0.23) vs 0.02 (95% CI, 0.00-0.04) in the intervention phase, resulting in an adjusted IR of 0.13 (95% CI, 0.10-0.18)., Discussion: Incidence of FTRMEs, as well as the IR, is comparable to previous studies., Conclusion: The intervention resulted in a substantial reduction in the incidence of FTRMEs., (© 2020 The Authors. Journal of Parenteral and Enteral Nutrition published by Wiley Periodicals, Inc. on behalf of American Society for Parenteral and Enteral Nutrition.)

Background: Falling is a common and serious problem in the elderly. Previous studies suggest that the use of psychotropic drugs increases the risk of falling. However, the contribution of these drugs on fall risk has not been quantified on a daily basis among the general population of nursing homes until now., Objective: To assess the association between fall incidence and the prescription of psychotropic drugs and different categories of psychotropic drugs (antipsychotics, antidepressants, and benzodiazepines) among a general nursing home population., Design: Retrospective observational study, data collection per person-day., Setting: 9 nursing homes in Eindhoven, the Netherlands., Participants: 2368 nursing home residents, resulting in 538,575 person-days., Main Outcome Measure: Association between the prescription of psychotropic drugs and falls., Results: A total of 2368 nursing home residents were included, which resulted in a data set of 538,575 person-days. Prescription of at least 1 psychotropic drug per day occurred during a total of 318,128 person-days (59.1%). Scheduled prescriptions with or without an as-needed prescription were involved in a total of 270,781 person-days (50.3%). The prescription of psychotropic drugs on a scheduled basis was found to be associated with almost a 3-fold increase in fall incidence (OR 2.88; 95% CI 1.52-5.44). An increase in fall incidence was found following the prescription of antipsychotics (OR 1.97; 95% CI 1.51-2.59) and antidepressants (OR 2.26; 95% CI 1.73-2.95). This increased fall risk was found for prescriptions on a scheduled basis as well as for prescriptions on an as-needed basis., Conclusion: The prescription of psychotropic drugs is associated with a strongly increased risk of falling among nursing home residents. To our knowledge, this is the first study among the general nursing home population in which the association between daily falls and daily prescriptions of psychotropic drugs and groups of psychotropic drugs was specified., (Copyright © 2016. Published by Elsevier Inc.)