Use of an abnormal laboratory value-drug combination alert to detect drug-induced thrombocytopenia in critically Ill patients

Detection of Drug-Induced Thrombocytopenia Signals in Children Using Routine Electronic Medical Records

Background: Drug-induced thrombocytopenia (DITP) is a severe adverse reaction and a significantly under-recognized clinical problem in children. However, for post-marketing pharmacovigilance purposes, detection of DITP signals is crucial. This study aimed to develop a signal detection model for DITP using the pediatric electronic medical records (EMR) data.

Methods: This study used the electronic medical records collected at Beijing Children’s Hospital between 2009 and 2020. A two-stage modeling method was developed to detect the signal of DITP. In the first stage, we calculated the crude incidence by mining cases of thrombocytopenia to select the potential suspected drugs. In the second stage, we constructed propensity score–matched retrospective cohorts of specific screened drugs from the first stage and estimated the odds ratio (OR) and 95% confidence interval (CI) using conditional logistic regression models. The novelty of the signal was assessed by current evidence.

Results: In the study, from a total of 839 drugs, 21 drugs were initially screened as potentially inducing thrombocytopenia. In total, we identified 18 positive DITP associations. Of these, potential DITP risk of nystatin (OR: 1.75, 95% CI: 1.37–2.22) and latamoxef sodium (OR: 1.61, 95% CI: 1.38–1.88) were two new DITP signals in both children and adults. Six associations between thrombocytopenia and drugs including imipenem (OR: 1.69, 95% CI: 1.16–2.45), teicoplanin (OR: 4.75, 95% CI: 3.33–6.78), fusidic acid (OR: 2.81, 95% CI: 2.06–3.86), ceftizoxime sodium (OR: 1.83, 95% CI: 1.36–2.45), ceftazidime (OR: 2.16, 95% CI: 1.58–2.95), and cefepime (OR: 5.06, 95% CI: 3.77–6.78) were considered as new signals in children.

Conclusion: This study developed a two-stage algorithm to detect safety signals of DITP and found eighteen positive signals of DITP, including six new signals in a pediatric population. This method is a promising tool for pharmacovigilance based on EMR data.

Transforming the Medication Regimen Review Process Using Telemedicine to Prevent Adverse Events

BACKGROUND/OBJECTIVES

Federally-mandated consultant pharmacist-conducted retrospective medication regimen reviews (MRRs) are designed to improve medication safety in nursing homes (NH). However, MRRs are potentially ineffective. A new model of care that improves access to and efficiency of consultant pharmacists is needed. The objective of this study was to determine the impact of pharmacist-led telemedicine services on reducing high-risk medication adverse drug events (ADEs) for NH residents using medication reconciliation and prospective MRR on admission plus ongoing clinical decision support alerts throughout the residents' stay.

DESIGN

Quality improvement study using a stepped-wedge design comparing the novel service to usual care in a one-year evaluation from November 2016 to October 2017.

SETTING

Four NHs (two urban, two suburban) in Southwestern Pennsylvania.

PARTICIPANTS

All residents in the four NHs were screened. There were 2,127 residents admitted having 652 alerts in the active period.

INTERVENTION

Upon admission, pharmacists conducted medication reconciliation and prospective MRR for residents and also used telemedicine for communication with cognitively-intact residents. Post-admission, pharmacists received clinical decision support alerts to conduct targeted concurrent MRRs and telemedicine.

MEASUREMENT

Main outcome was incidence of high-risk medication, alert-specific ADEs. Secondary outcomes included all-cause hospitalization, 30-day readmission rates, and consultant pharmacists' recommendations.

RESULTS

Consultant pharmacists provided 769 recommendations. The intervention group had a 92% lower incidence of alert-specific ADEs than usual care (9 vs 31; 0.14 vs 0.61/1,000-resident-days; adjusted incident rate ratio (AIRR) = 0.08 (95% confidence interval (CI) = 0.01-0.40]; P = .002). All-cause hospitalization was similar between groups (149 vs 138; 2.33 vs 2.70/1,000-resident-days; AIRR = 1.06 (95% CI = 0.72-1.58); P = .75), as were 30-day readmissions (110 vs 102; 1.72 vs 2.00/1,000-resident-days; AIRR = 1.21 (95% CI = 0.76-1.93); P = .42).

CONCLUSIONS

This is the first evaluation of the impact of pharmacist-led patient-centered telemedicine services to manage high-risk medications during transitional care and throughout the resident's NH stay, supporting a new model of patient care.

Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination

BACKGROUND

Although clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)-integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the clinician's interaction with these alerts in general.

OBJECTIVE

This study aimed to describe the development and implementation of an ML-based signal-to-noise optimization system (SmartCDS) to increase the signal of alerts by decreasing the volume of low-value herpes zoster (shingles) vaccination alerts.

METHODS

We built and deployed SmartCDS, which builds personalized user activity profiles to suppress shingles vaccination alerts unlikely to yield a clinician's interaction. We extracted all records of shingles alerts from January 2017 to March 2019 from our EHR system, including 327,737 encounters, 780 providers, and 144,438 patients.

RESULTS

During the 6 weeks of pilot deployment, the SmartCDS system suppressed an average of 43.67% (15,425/35,315) potential shingles alerts (appointments) and maintained stable counts of weekly shingles vaccination orders (326.3 with system active vs 331.3 in the control group; P=.38) and weekly user-alert interactions (1118.3 with system active vs 1166.3 in the control group; P=.20).

CONCLUSIONS

All key statistics remained stable while the system was turned on. Although the results are promising, the characteristics of the system can be subject to future data shifts, which require automated logging and monitoring. We demonstrated that an automated, ML-based method and data architecture to suppress alerts are feasible without detriment to overall order rates. This work is the first alert suppression ML-based model deployed in practice and serves as foundational work in encounter-level customization of alert display to maximize effectiveness.

Effect of Best Practice Advisories on Sedation Protocol Compliance and Drug-Related Hazardous Condition Mitigation Among Critical Care Patients

OBJECTIVES

To determine whether best practice advisories improved sedation protocol compliance and could mitigate potential propofol-related hazardous conditions.

DESIGN

Retrospective observational cohort study.

SETTING

Two adult ICUs at two academic medical centers that share the same sedation protocol.

PATIENTS

Adults 18 years old or older admitted to the ICU between January 1, 2016, and January 31, 2018, who received a continuous infusion of propofol.

INTERVENTIONS

Two concurrent best practice advisories built in the electronic health record as a clinical decision support tool to enforce protocol compliance with triglyceride and lipase level monitoring and mitigate propofol-related hazardous conditions.

MEASUREMENTS AND MAIN RESULTS

The primary outcomes were baseline and day 3 compliance with triglyceride and lipase laboratory monitoring per protocol and time to discontinuation of propofol in the setting of triglyceride and/or lipase levels exceeding protocol cutoffs. A total of 1,394 patients were included in the study cohort (n = 700 in the pre-best practice advisory group; n = 694 in the post-best practice advisory group). In inverse probability weighted regression analyses, implementing the best practice advisory was associated with a 56.6% (95% CI, 52.6-60.9) absolute increase and a 173% relative increase (risk ratio, 2.73; 95% CI, 2.45-3.04) in baseline laboratory monitoring. The best practice advisory was associated with a 34.0% (95% CI, 20.9-47.1) absolute increase and a 74% (95% CI, 1.39-2.19) relative increase in day 3 laboratory monitoring after inverse probability weighted analyses. Among patients with laboratory values exceeding protocol cutoffs, implementation of the best practice advisory resulted in providers discontinuing propofol an average of 16.6 hours (95% CI, 4.8-28.3) sooner than pre-best practice advisory. Findings from alternate analyses using interrupted time series were consistent with the inverse probability weighted analyses.

CONCLUSIONS

Best practice advisories can be effectively used in ICUs to improve sedation protocol compliance and may mitigate potential propofol-related hazardous conditions. Best practice advisories should undergo continuous quality assurance and optimizations to maximize clinical utility and minimize alert fatigue.

Trigger alerts associated with laboratory abnormalities on identifying potentially preventable adverse drug events in the intensive care unit and general ward

Background

Medication safety strategies involving trigger alerts have demonstrated potential in identifying drug-related hazardous conditions (DRHCs) and preventing adverse drug events in hospitalized patients. However, trigger alert effectiveness between intensive care unit (ICU) and general ward patients remains unknown. The objective was to investigate trigger alert performance in accurately identifying DRHCs associated with laboratory abnormalities in ICU and non-ICU settings.

Methods

This retrospective, observational study was conducted at a university hospital over a 1-year period involving 20 unique trigger alerts aimed at identifying possible drug-induced laboratory abnormalities. The primary outcome was to determine the positive predictive value (PPV) in distinguishing drug-induced abnormal laboratory values using trigger alerts in critically ill and general ward patients. Aberrant lab values attributed to medications without resulting in an actual adverse event ensuing were categorized as a DRHC.

Results

A total of 634 patients involving 870 trigger alerts were included. The distribution of trigger alerts generated occurred more commonly in general ward patients (59.8%) than those in the ICU (40.2%). The overall PPV in detecting a DRHC in all hospitalized patients was 0.29, while the PPV in non-ICU patients (0.31) was significantly higher than the critically ill (0.25) (p = 0.03). However, the rate of DRHCs was significantly higher in the ICU than the general ward (7.49 versus 0.87 events per 1000 patient days, respectively, p < 0.0001). Although most DRHCs were considered mild or moderate in severity, more serious and life-threatening DRHCs occurred in the ICU compared with the general ward (39.8% versus 12.4%, respectively, p < 0.001).

Conclusions

Overall, most trigger alerts performed poorly in detecting DRHCs irrespective of patient care setting. Continuous process improvement practices should be applied to trigger alert performance to improve clinician time efficiency and minimize alert fatigue.

Quality of Decision Support in Computerized Provider Order Entry: Systematic Literature Review

Background

Computerized decision support systems have raised a lot of hopes and expectations in the field of order entry. Although there are numerous studies reporting positive impacts, concerns are increasingly high about alert fatigue and effective impacts of these systems. One of the root causes of fatigue alert reported is the low clinical relevance of these alerts.

Objective

The objective of this systematic review was to assess the reported positive predictive value (PPV), as a proxy to clinical relevance, of decision support systems in computerized provider order entry (CPOE).

Methods

A systematic search of the scientific literature published between February 2009 and March 2015 on CPOE, clinical decision support systems, and the predictive value associated with alert fatigue was conducted using PubMed database. Inclusion criteria were as follows: English language, full text available (free or pay for access), assessed medication, direct or indirect level of predictive value, sensitivity, or specificity. When possible with the information provided, PPV was calculated or evaluated.

Results

Additive queries on PubMed retrieved 928 candidate papers. Of these, 376 were eligible based on abstract. Finally, 26 studies qualified for a full-text review, and 17 provided enough information for the study objectives. An additional 4 papers were added from the references of the reviewed papers. The results demonstrate massive variations in PPVs ranging from 8% to 83% according to the object of the decision support, with most results between 20% and 40%. The best results were observed when patients’ characteristics, such as comorbidity or laboratory test results, were taken into account. There was also an important variation in sensitivity, ranging from 38% to 91%.

Conclusions

There is increasing reporting of alerts override in CPOE decision support. Several causes are discussed in the literature, the most important one being the clinical relevance of alerts. In this paper, we tried to assess formally the clinical relevance of alerts, using a near-strong proxy, which is the PPV of alerts, or any way to express it such as the rate of true and false positive alerts. In doing this literature review, three inferences were drawn. First, very few papers report direct or enough indirect elements that support the use or the computation of PPV, which is a gold standard for all diagnostic tools in medicine and should be systematically reported for decision support. Second, the PPV varies a lot according to the typology of decision support, so that overall rates are not useful, but must be reported by the type of alert. Finally, in general, the PPVs are below or near 50%, which can be considered as very low.

Technologic Distractions (Part 1): Summary of Approaches to Manage Alert Quantity With Intent to Reduce Alert Fatigue and Suggestions for Alert Fatigue Metrics

OBJECTIVE

To provide ICU clinicians with evidence-based guidance on tested interventions that reduce or prevent alert fatigue within clinical decision support systems.

DESIGN

Systematic review of PubMed, Embase, SCOPUS, and CINAHL for relevant literature from 1966 to February 2017.

PATIENTS

Focus on critically ill patients and included evaluations in other patient care settings, as well.

INTERVENTIONS

Identified interventions designed to reduce or prevent alert fatigue within clinical decision support systems.

MEASUREMENTS AND MAIN RESULTS

Study selection was based on one primary key question to identify effective interventions that attempted to reduce alert fatigue and three secondary key questions that covered the negative effects of alert fatigue, potential unintended consequences of efforts to reduce alert fatigue, and ideal alert quantity. Data were abstracted by two reviewers independently using a standardized abstraction tool. Surveys, meeting abstracts, "gray" literature, studies not available in English, and studies with non-original data were excluded. For the primary key question, articles were excluded if they did not provide a comparator as key question 1 was designed as a problem, intervention, comparison, and outcome question. We anticipated that reduction in alert fatigue, including the concept of desensitization may not be directly measured and thus considered interventions that reduced alert quantity as a surrogate marker for alert fatigue. Twenty-six articles met the inclusion criteria.

CONCLUSION

Approaches for managing alert fatigue in the ICU are provided as a result of reviewing tested interventions that reduced alert quantity with the anticipated effect of reducing fatigue. Suggested alert management strategies include prioritizing alerts, developing sophisticated alerts, customizing commercially available alerts, and including end user opinion in alert selection. Alert fatigue itself is studied less frequently, as an outcome, and there is a need for more precise evaluation. Standardized metrics for alert fatigue is needed to advance the field. Suggestions for standardized metrics are provided in this document.

Clinical Practice Guideline: Safe Medication Use in the ICU

OBJECTIVE

To provide ICU clinicians with evidence-based guidance on safe medication use practices for the critically ill.

DATA SOURCES

PubMed, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, CINAHL, Scopus, and ISI Web of Science for relevant material to December 2015.

STUDY SELECTION

Based on three key components: 1) environment and patients, 2) the medication use process, and 3) the patient safety surveillance system. The committee collectively developed Population, Intervention, Comparator, Outcome questions and quality of evidence statements pertaining to medication errors and adverse drug events addressing the key components. A total of 34 Population, Intervention, Comparator, Outcome questions, five quality of evidence statements, and one commentary on disclosure was developed.

DATA EXTRACTION

Subcommittee members were assigned selected Population, Intervention, Comparator, Outcome questions or quality of evidence statements. Subcommittee members completed their Grading of Recommendations Assessment, Development, and Evaluation of the question with his/her quality of evidence assessment and proposed strength of recommendation, then the draft was reviewed by the relevant subcommittee. The subcommittee collectively reviewed the evidence profiles for each question they developed. After the draft was discussed and approved by the entire committee, then the document was circulated among all members for voting on the quality of evidence and strength of recommendation.

DATA SYNTHESIS

The committee followed the principles of the Grading of Recommendations Assessment, Development, and Evaluation system to determine quality of evidence and strength of recommendations.

CONCLUSIONS

This guideline evaluates the ICU environment as a risk for medication-related events and the environmental changes that are possible to improve safe medication use. Prevention strategies for medication-related events are reviewed by medication use process node (prescribing, distribution, administration, monitoring). Detailed considerations to an active surveillance system that includes reporting, identification, and evaluation are discussed. Also, highlighted is the need for future research for safe medication practices that is specific to critically ill patients.

Clinical decision support for drug related events: Moving towards better prevention

Clinical decision support (CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors (ME) and adverse drug events (ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.

What Alert Thresholds Should Be Used to Identify Critical Risk Results: A Systematic Review of the Evidence

BACKGROUND

Pathology laboratories are required to immediately report results which indicate a patient is at critical risk, but there is little consensus about what values are deemed critical. The aim of this review was to systematically review the literature on alert thresholds for common chemistry and hematology tests in adults and to provide an explicit and ranked source of this evidence.

METHODS

The literature search covered the period of 1995–2014. Evidence sources were critically appraised and ranked using the 1999 Stockholm hierarchy for analytical performance specifications in laboratory medicine modified for establishing decision limits.

RESULTS

The 30 most frequently reported laboratory tests with alert thresholds are presented with evidence rankings. Similar thresholds were reported in North America, Europe and Asia. Seventy percent of papers reported thresholds set by individual institutions, while 18% contained thresholds from surveys of laboratories or clinicians. Forty-six percent of the papers referred to 1 or both of the 2 American laboratory surveys from the early 1990s. “Starter sets” of alert thresholds were recommended by 6 professional bodies, 3 of which were collaborations between pathologists and clinicians. None of the 9 outcome studies identified dealt with confounding factors.

CONCLUSIONS

Recommendations by professional bodies based on outdated surveys of the former state of the art or consensus are currently the best sources of evidence for laboratories to build their alert list. Well-designed outcome studies and greater collaboration between clinicians and the laboratory are needed to identify the most appropriate alert thresholds that signify actionable, critical or significant risk to patient well-being.

An Evaluation of Adverse Drug Reactions Associated With Antipsychotic Use for the Treatment of Delirium in the Intensive Care Unit

Purpose:

This investigation evaluated the incidence, severity, and harm of adverse drug reactions (ADRs) associated with antipsychotic use for intensive care unit (ICU) delirium.

Methods:

In this prospective, observational study patients were screened for development of delirium with the Intensive Care Delirium Screening Checklist (ICDSC). An ICDSC score of ≥4 was considered delirious. Patients with delirium were screened daily for ADRs. Suspected ADRs were evaluated for drug causality using 3 published, objective assessment tools. Suspected ADRs were considered positive when 2 of 3 instruments had an agreement rating of “possible” or greater. ADR severity was defined as “mild/moderate” or “severe” using the National Cancer Institute’s Common Terminology Criteria for Adverse Events scale. A modified National Coordinating Council Medication Error Reporting Index for Categorizing Errors categorized ADRs into “no harm” or “harmful.”

Results:

Of 90 patients with delirium, 56 received antipsychotics. Ten suspected ADRs occurred attributed to antipsychotic use. QTc prolongation was the most observed ADR (50%). Patients with ADRs had higher mean Acute Physiology and Chronic Health Evaluation II (APACHE II) scores ( P = .038). Patients who received haloperidol experienced more severe ( P = .048) ADRs.

Conclusions:

ADRs were observed in 18% of patients having delirium treated with antipsychotics with about half considered severe or harmful. A risk versus benefit assessment is needed before initiating antipsychotic therapy in ICU patients.

Evaluation of an automated surveillance system using trigger alerts to prevent adverse drug events in the intensive care unit and general ward

INTRODUCTION

Adverse events in the intensive care unit (ICU) may be associated with several possible causes, so determining a drug-related causal assessment is more challenging than in general ward patients. Therefore, the hypothesis was that automated trigger alerts may perform differently in various patient care settings. The purpose of this study was to compare the frequency and type of clinically significant automated trigger alerts in critically ill and general ward patients as well as evaluate the performance of alerts for drug-related hazardous conditions (DRHCs).

METHODS

A retrospective cohort study was conducted in adult ICU and general ward patients at three institutions (academic, community, and rural hospital) in a health system. Automated trigger alerts generated during two nonconsecutive months were obtained from a centralized database. Pharmacist responses to alerts and prescriber response to recommendations were evaluated for all alerts. A clinical significant event was defined as an actionable intervention requiring drug therapy changes that the pharmacist determined to be appropriate for patient safety and where the physician accepted the pharmacist's recommendation. The positive predictive value (PPV) was calculated for each trigger alert considered a DRHC (i.e., abnormal laboratory values and suspected drug causes).

RESULTS

A total of 751 alerts were generated in 623 patients during the study period. Pharmacists intervened on 39.8 and 44.8 % alerts generated in the ICU and general ward, respectively. Overall, the physician acceptance rate of approximately 90 % was comparable irrespective of patient care setting. Therefore, the number of clinically significant alerts was 88.9 and 83.4 % for the ICU and non-ICU, respectively. The types of drug therapy changes were similar between settings. The PPV of alerts identifying a DRHC was 0.66 in the ICU and 0.76 in general ward patients.

CONCLUSIONS

The number and type of clinically significant alerts were similar irrespective of patient population, suggesting that the alerts may be equally as beneficial in the ICU population, despite the challenges in drug-related event adjudication. An opportunity exists to improve the performance of alerts in both settings, so quality improvement programs for measuring alert performance and making refinements is needed.

Utilization of a Pharmacy Clinical Surveillance System for Pharmacist Alerting and Communication at a Tertiary Academic Medical Center

The objective of this analysis is to describe the utilization metrics of a pharmacy clinical surveillance system (PCSS) at a tertiary, academic medical center.We performed a retrospective database analysis assessing rule-based alerts (RBA), interventions and pharmacist communication notes documented in the PCSS from January 1, 2014 to December 31, 2014. Reports were generated on 92 unique RBAs sent to clinicians for evaluation. Metrics assessed included the number of RBAs that were triggered, clinically evaluated, intervened on by pharmacists, and therapeutic category of interventions. Pharmacy communication notes were also evaluated.A total of 399,979 RBAs were triggered through the PCSS. During that time, pharmacists documented a total of 17,733 interventions. The most common RBAs were related to lab abnormalities (132,487; 33 %) and anticoagulation/antiplatelet therapy (126,425; 32.1 %). Interventions were most frequently related to RBAs regarding anticoagulation/antiplatelet therapy (6412; 36 %) and antimicrobial therapy (3320; 19 %). Pharmacist communication was most commonly related to clarification of medication and lab orders, and therapeutic drug monitoring.Based on utilization metrics presented, the implementation of a PCSS has successfully generated RBAs to aid pharmacists in clinical practice and improved departmental documentation and communication. Further analysis is warranted to assess the impact of the RBAs, interventions, and communication notes on outcomes such as hospital cost and adverse drug events.

A Multicenter Evaluation of Off-Label Medication Use and Associated Adverse Drug Reactions in Adult Medical ICUs

OBJECTIVE

Prior research indicates that off-label use is common in the ICU; however, the safety of off-label use has not been assessed. The study objective was to determine the prevalence of adverse drug reactions associated with off-label use and evaluate off-label use as a risk factor for the development of adverse drug reactions in an adult ICU population.

DESIGN

Multicenter, observational study

SETTING

: Medical ICUs at three academic medical centers.

PATIENTS

Adult patients (age ≥ 18 yr old) receiving medication therapy.

INTERVENTIONS

All administered medications were evaluated for Food and Drug Administration-approved or off-label use. Patients were assessed daily for the development of an adverse drug reaction through active surveillance. Three adverse drug reaction assessment instruments were used to determine the probability of an adverse drug reaction resulting from drug therapy. Severity and harm of the adverse drug reaction were also assessed. Cox proportional hazard regression was used to identify a set of covariates that influenced the rate of adverse drug reactions.

MEASUREMENTS AND MAIN RESULTS

Overall, 1,654 patient-days (327 patients) and 16,391 medications were evaluated, with 43% of medications being used off-label. One hundred and sixteen adverse drug reactions were categorized dichotomously (Food and Drug Administration or off-label), with 56% and 44% being associated with Food and Drug Administration-approved and off-label use, respectively. The number of adverse drug reactions for medications administered and the number of harmful and severe adverse drug reactions did not differ for medications used for Food and Drug Administration-approved or off-label use (0.74% vs 0.67%; p = 0.336; 33 vs 31 events, p = 0.567; 24 vs 24 events, p = 0.276). Age, sex, number of high-risk medications, number of off-label medications, and severity of illness score were included in the Cox proportional hazard regression. It was found that the rate of adverse drug reactions increases by 8% for every one additional off-label medication (hazard ratio = 1.08; 95% CI, 1.018-1.154).

CONCLUSION

Although adverse drug reactions do not occur more frequently with off-label use, adverse drug reaction risk increases with each additional off-label medication used.

Reducing PSA-Based Prostate Cancer Screening in Men Aged 75 Years and Older with the Use of Highly Specific Computerized Clinical Decision Support

INTRODUCTION

In 2012, the Veterans Health Administration (VHA) implemented guidelines seeking to reduce PSA-based screening for prostate cancer in men aged 75 years and older.

OBJECTIVES

To reduce the use of inappropriate PSA-based prostate cancer screening among men aged 75 and over.

SETTING

The Veterans Affairs Greater Los Angeles Healthcare System (VA GLA) PROGRAM DESCRIPTION: We developed a highly specific computerized clinical decision support (CCDS) alert to remind providers, at the moment of PSA screening order entry, of the current guidelines and institutional policy. We implemented the tool in a prospective interrupted time series study design over 15 months, and compared the trends in monthly PSA screening rate at baseline to the CCDS on and off periods of the intervention.

RESULTS

A total of 30,150 men were at risk, or eligible, for screening, and 2,001 men were screened. The mean monthly screening rate during the 15-month baseline period was 8.3%, and during the 15-month intervention period, was 4.6%. The screening rate declined by 38% during the baseline period and by 40% and 30%, respectively, during the two periods when the CCDS tool was turned on. The screening rate ratios for the baseline and two periods when the CCDS tool was on were 0.97, 0.78, and 0.90, respectively, with a significant difference between baseline and the first CCDS-on period (p < 0.0001), and a trend toward a difference between baseline and the second CCDS-on period (p = 0.056).

CONCLUSION

Implementation of a highly specific CCDS tool alone significantly reduced inappropriate PSA screening in men aged 75 years and older in a reproducible fashion. With this simple intervention, evidence-based guidelines were brought to bear at the point of care, precisely for the patients and providers for whom they were most helpful, resulting in more appropriate use of medical resources.

Use of a Clinical Decision Support System Alert to Prevent Supratherapeutic Vancomycin Concentrations

Background: Alerts issued by clinical decision support systems (CDSS) may be useful to identify and prevent the occurrence of acute kidney injury among patients on nephrotoxic drugs, particularly vancomycin. Objective: The purpose of this instructive study was to determine the effectiveness of using a pharmacist-run CDSS alert of early serum creatinine increases in patients receiving intravenous vancomycin to decrease the proportion of severely elevated vancomycin concentrations. Methods: This was a retrospective study of a prospectively reviewed CDSS alert that triggered in patients with an increase in serum creatinine by 25% from baseline within 24 hours. Severely elevated vancomycin concentrations were divided into a control group (before alert implementation) and a study group (after alert implementation) and considered for study inclusion. The proportion of severely elevated vancomycin concentrations (ie, >30 mg/L) were collected in the control and study groups. Results: There were 1290 and 1501 vancomycin concentrations in the control group and the study group, respectively. A total of 696 CDSS alerts triggered during the study period. The proportion of severely elevated vancomycin troughs decreased from 5.3% (n = 68, median = 36.6 mg/L, interquartile range = 33.75-43.2 mg/L) in the control group to 3.7% (n = 55, median = 34.7 mg/L, interquartile range = 31.3-39.3 mg/L) in the study group. This reflects a statistically significant decrease in the proportion of severely elevated vancomycin concentrations ( P = .04). Conclusion: Overall, this instructive analysis on a novel use of CDSS software suggests that the implementation of an alert based on early detection of serum creatinine changes led to a significant decrease in the proportion of severely elevated serum vancomycin concentrations.

Evaluation of real-time clinical decision support systems for platelet and cryoprecipitate orders

OBJECTIVES

To evaluate cryoprecipitate and platelet ordering practices after the implementation of real-time clinical decision support systems (CDSSs) in a computerized physician order entry (CPOE) system.

METHODS

Uniform platelet and cryoprecipitate transfusion thresholds were implemented at 11 hospitals in a regional health care system with a common CPOE system. Over 6 months, a variety of information was collected on the ordering physicians and the number of alerts generated by the CDSSs when these products were ordered outside of the institutional guidelines.

RESULTS

There were 1,889 orders for platelets and 152 orders for cryoprecipitate placed in 6 months. Of these, 1,102 (58.3%) platelet and 74 (48.7%) cryoprecipitate orders triggered an alert. The proportion of orders canceled after an alert was generated ranged from 13.5% to 17.9% for platelets and 0% to 50.0% for cryoprecipitate orders.

CONCLUSIONS

CDSS alerts reduce, but do not eliminate, platelet and cryoprecipitate transfusions that do not meet institutional guidelines.

Clinical decision support for atypical orders: detection and warning of atypical medication orders submitted to a computerized provider order entry system

The specificity of medication-related alerts must be improved to overcome the pernicious effects of alert fatigue. A systematic comparison of new drug orders to historical orders could improve alert specificity and relevance. Using historical order data from a computerized provider order entry system, we alerted physicians to atypical orders during the prescribing of five medications: calcium, clopidogrel, heparin, magnesium, and potassium. The percentage of atypical orders placed for these five medications decreased during the 92 days the alerts were active when compared to the same period in the previous year (from 0.81% to 0.53%; p=0.015). Some atypical orders were appropriate. Fifty of the 68 atypical order alerts were over-ridden (74%). However, the over-ride rate is misleading because 28 of the atypical medication orders (41%) were changed. Atypical order alerts were relatively few, identified problems with frequencies as well as doses, and had a higher specificity than dose check alerts.

Computerized decision support in adult and pediatric critical care

Computerized decision support (CDS) is the most advanced form of clinical decision support available and has evolved with innovative technologies to provide meaningful assistance to medical professionals. Critical care clinicians are in unique environments where vast amounts of data are collected on individual patients, and where expedient and accurate decisions are paramount to the delivery of quality healthcare. Many CDS tools are in use today among adult and pediatric intensive care units as diagnostic aides, safety alerts, computerized protocols, and automated recommendations for management. Some CDS use have significantly decreased adverse events and improved costs when carefully implemented and properly operated. CDS tools integrated into electronic health records are also valuable to researchers providing rapid identification of eligible patients, streamlining data-gathering and analysis, and providing cohorts for study of rare and chronic diseases through data-warehousing. Although the need for human judgment in the daily care of critically ill patients has limited the study and realization of meaningful improvements in overall patient outcomes, CDS tools continue to evolve and integrate into the daily workflow of clinicians, and will likely provide advancements over time. Through novel technologies, CDS tools have vast potential for progression and will significantly impact the field of critical care and clinical research in the future.

Detection and correct handling of prescribing errors in Dutch hospital pharmacies using test patients

BACKGROUND

Hospital pharmacists and pharmacy technicians play a major role in detecting prescribing errors by medication surveillance. At present the frequency of detected and correctly handled prescribing errors is unclear, as are factors associated with correct handling.

OBJECTIVE

To examine the frequency of detection of prescribing errors and the frequency of correct handling, as well as factors associated with correct handling of prescribing errors by hospital pharmacists and pharmacy technicians.

SETTING

This study was conducted in 57 Dutch hospital pharmacies.

METHOD

Prospective observational study with test patients, using a case-control design to identify factors associated with correct handling. A questionnaire was used to collect the potential factors. Test patients containing prescribing errors were developed by an expert panel of hospital pharmacists (a total of 40 errors in nine medication records divided among three test patients; each test patient was used in 3 rounds; on average 4.5 prescribing error per patient per round). Prescribing errors were defined as dosing errors or therapeutic errors (contra-indication, drug-drug interaction, (pseudo)duplicate medication). The errors were selected on relevance and unequivocalness. The panel also defined how the errors should be handled in practice using national guidelines and this was defined as 'correct handling'. The test patients had to be treated as real patients while conducting medication surveillance. The pharmacists and technicians were asked to report detected errors to the investigator.

MAIN OUTCOME MEASURE

The percentages of detected and correctly handled prescribing errors were the main outcome measures. Factors associated with correct handling were determined, using multivariate logistic regression analysis.

RESULTS

Fifty-nine percent of the total number of intentionally added prescribing errors were detected and 57 % were handled correctly by the hospital pharmacists and technicians. The use of a computer system for medication surveillance compared to no computer system was independently associated with correct handling [odds ratio (OR) 15.39 (95 % confidence interval (CI) 3.62-65.50] for computerized physician order entry system; OR 15.40 (95 % CI 3.61-65.70) for order entry by pharmacy technicians), but because the reference category contained only one hospital these results can't be interpreted. Furthermore, manual screening of dosages in children with or without computerized surveillance compared to no dosage checks for children [OR 2.02 (95 % CI 1.06-3.84)], qualified pharmacy technicians compared to no qualified pharmacy technicians [OR 1.32 (95 % CI 1.03-1.67)] and pharmacy technicians using protocols compared to ones not using protocols [OR 1.30 (95 % CI 1.04-1.61)] were independently associated with correct handling.

CONCLUSION

This study showed that the quality of medication surveillance in Dutch hospital pharmacies can be subject to improvement and the identified factors may give direction to such improvements.

Use of health information technology to reduce diagnostic errors

BACKGROUND

Health information technology (HIT) systems have the potential to reduce delayed, missed or incorrect diagnoses. We describe and classify the current state of diagnostic HIT and identify future research directions.

METHODS

A multi-pronged literature search was conducted using PubMed, Web of Science, backwards and forwards reference searches and contributions from domain experts. We included HIT systems evaluated in clinical and experimental settings as well as previous reviews, and excluded radiology computer-aided diagnosis, monitor alerts and alarms, and studies focused on disease staging and prognosis. Articles were organised within a conceptual framework of the diagnostic process and areas requiring further investigation were identified.

RESULTS

HIT approaches, tools and algorithms were identified and organised into 10 categories related to those assisting: (1) information gathering; (2) information organisation and display; (3) differential diagnosis generation; (4) weighing of diagnoses; (5) generation of diagnostic plan; (6) access to diagnostic reference information; (7) facilitating follow-up; (8) screening for early detection in asymptomatic patients; (9) collaborative diagnosis; and (10) facilitating diagnostic feedback to clinicians. We found many studies characterising potential interventions, but relatively few evaluating the interventions in actual clinical settings and even fewer demonstrating clinical impact.

CONCLUSIONS

Diagnostic HIT research is still in its early stages with few demonstrations of measurable clinical impact. Future efforts need to focus on: (1) improving methods and criteria for measurement of the diagnostic process using electronic data; (2) better usability and interfaces in electronic health records; (3) more meaningful incorporation of evidence-based diagnostic protocols within clinical workflows; and (4) systematic feedback of diagnostic performance.

Improving adverse drug event detection in critically ill patients through screening intensive care unit transfer summaries

PURPOSE

This study aimed to determine the frequency and type of adverse drug events (ADEs) identified in intensive care unit (ICU) transfer summaries and in the hospital discharge summaries to demonstrate the effectiveness of ICU transfer summary surveillance in the identification of ADEs.

METHODS

A retrospective electronic medical record review was conducted for medical ICU patients admitted between January 2009 and April 2009 to a large, academic medical center. The Harvard Practice Scale and the modified Leonard Assessment Scale were used to evaluate the presence of an ADE from the ICU transfer and hospital discharge summaries.

RESULTS

Two hundred and fifty-four patients were identified for inclusion with a median medical ICU length of stay of 4.5 days and hospital length of stay of 13 days. The ICU transfer summary review revealed 173 ADEs among 124 unique patients with a rate of 33.9 ADEs per 1000 hospital patient days. Sixty-nine ADEs among 63 unique patients were identified through the hospital discharge summary with a rate of 13.5 ADEs per 1000 hospital patient days. Only 23.1% of ADEs discussed in the ICU transfer summary were also discussed in the hospital discharge summary.

CONCLUSIONS

The use of ICU transfer summaries is an effective tool to increase ADE detection. The use of an ICU transfer summary should be considered as an adjunct method to an existing ADE surveillance system for heightened pharmacovigilance.

Drug-drug interactions contributing to QT prolongation in cardiac intensive care units

PURPOSE

To determine the most common drug-drug interaction (DDI) pairs contributing to QTc prolongation in cardiac intensive care units (ICUs).

MATERIALS AND METHODS

This retrospective evaluation included patients who were admitted to the cardiac ICUs between January 2009 and July 2009 aged ≥ 18 years with electrocardiographic evidence of a QTc ≥ 500 ms. Patients receiving at least two concomitant drugs known to prolong the QT interval were considered to experience a pharmacodynamic DDI. Drugs causing CYP450 inhibition of the metabolism of QT prolonging medications were considered to cause pharmacokinetic DDIs. The causality between drug and QTc prolongation was evaluated with an objective scale.

RESULTS

One hundred eighty-seven patients experienced QT prolongation out of a total of 501 patients (37%) admitted during the study period. Forty-three percent and 47% of patients experienced 133 and 179 temporally-related pharmacodynamic and pharmacokinetic interactions, respectively. The most common medications related to these DDIs were ondansetron, amiodarone, metronidazole, and haloperidol.

CONCLUSION

DDIs may be a significant cause of QT prolongation in cardiac ICUs. These data can be used to educate clinicians on safe medication use. Computerized clinical decision support could be applied to aid in the detection of these events.