Performance of an Electronic Decision Support System as a Therapeutic Intervention During a Multicenter PICU Clinical Trial: Heart and Lung Failure-Pediatric Insulin Titration Trial (HALF-PINT).

BACKGROUND: The use of electronic clinical decision support (CDS) systems for pediatric critical care trials is rare. We sought to describe in detail the use of a CDS tool (Children's Hospital Euglycemia for Kids Spreadsheet [CHECKS]), for the management of hyperglycemia during the 32 multicenter Heart And Lung Failure-Pediatric Insulin Titration trial. RESEARCH QUESTION: In critically ill pediatric patients who were treated with CHECKS, how was user compliance associated with outcomes; and what patient and clinician factors might account for the observed differences in CHECKS compliance? STUDY DESIGN AND METHODS: During an observational retrospective study of compliance with a CDS tool used during a prospective randomized controlled trial, we compared patients with high and low CHECKS compliance. We investigated the association between compliance and blood glucose metrics. We describe CHECKS and use a computer interface analysis framework (the user, function, representation, and task analysis framework) to categorize user interactions. We discuss implications for future randomized controlled trials. RESULTS: Over a 4.5-year period, 658 of 698 children were treated with the CHECKS protocol for ≥24 hours with a median of 119 recommendations per patient. Compliance per patient was high (median, 99.5%), with only 30 patients having low compliance (<90%). Patients with low compliance were from 16 of 32 sites, younger (P = .02), and less likely to be on inotropic support (P = .04). They were more likely to be have been assigned randomly to the lower blood glucose target (80% vs 48%; P < .001) and to have spent a shorter time (53% vs 75%; P < .001) at the blood glucose target. Overrides (classified by the user, function, representation, and task analysis framework), were largely (89%) due to the user with patient factors contributing 29% of the time. INTERPRETATION: The use of CHECKS for the Heart And Lung Failure-Pediatric Insulin Titration trial resulted in a highly reproducible and explicit method for the management of hyperglycemia in critically ill children across varied environments. CDS systems represent an important mechanism for conducting explicit complex pediatric critical care trials. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01565941, registered March 29 2012; https://clinicaltrials.gov/ct2/show/NCT01565941?term=HALF-PINT&draw=2&rank=1.

Bolus pharmacokinetics: moving beyond mass-based dosing to guide drug administration.

Despite the common approach of bolus drug dosing using a patient's mass, a more tailored approach would be to use empirically derived pharmacokinetic models. Previously, this could only be possible though the use of computer simulation using programs which are rarely available in clinical practice. Through mathematical manipulations and approximations, a simplified set of equations is demonstrated that can identify a bolus dose required to achieve a specified target effect site concentration. The proposed solution is compared against simulations of a wide variety of pharmacokinetic models. This set of equations provides a near-identical solution to the simulation approach. A boundary condition is established to ensure the derived equations have an acceptable error. This approach may allow for more precise administration of medications with the use of point of care technology and potentially allows for pharmacokinetic dosing in artificial intelligence problems.

CT fluoroscopy-guided transforaminal and intra-articular facet steroid injections for the treatment of cervical radiculopathy: injectate distribution patterns and association with clinical outcome.

OBJECTIVES: To investigate injectate dispersal patterns and their association with therapeutic efficacy during a transforaminal (TFSI) or an intra-articular facet steroid injection (IFSI) to treat cervical radiculopathy. METHODS: This retrospective study examined the post-intervention cervical spine CT of 56 patients randomized to receive one CT fluoroscopy-guided IFSI (29 patients; 10 (34.5%) males; mean age 45.0 years; SD 8.8 years; range 26-61 years) or TFSI (27 patients; 13 (48.2%) males; mean age 51.1 years; SD 11.2 years; range 29-72 years) (December 2010 to August 2013). The presence of contrast within the intra-articular facet, juxta-articular facet, retrodural, epidural, and foraminal and extraforaminal spaces during IFSI, and within the extraforaminal, foraminal, and epidural spaces during TFSI was assessed. Descriptive data are presented as frequencies. The association between injectate dispersal patterns and therapeutic efficacy, 4-week post-intervention, was assessed with ANCOVA models. RESULTS: During IFSI, the injectate predominantly spread to the retrodural (62%; 18/29) or juxta-articular (21%; 6/29) space. During TFSI, the injectate predominantly spread to the extraforaminal/foraminal spaces (41%; 11/27) or to the extraforaminal/foraminal/epidural spaces (33%; 9/27). Injectate presence in the juxta-articular (p = .007) or extraforaminal (p < .001) space was a predictor of therapeutic efficacy but not in the foraminal (p = .54), epidural (p = .89), or retrodural (p = .75) space. CONCLUSIONS: TFSI and IFSI led to preferential extraforaminal and retrodural injectate spread, respectively. Targeting the extraforaminal or juxta-articular facet space improved the clinical efficacy of steroid injections when treating cervical radiculopathy. KEY POINTS: • During intra-articular facet injection, the injectate spreads from the facet joint to the retrodural space and rarely reaches the epidural and/or foraminal spaces. • Epidural spread of the injectate during an anterolateral transforaminal steroid injection is the least effective for pain relief in patients with cervical radiculopathy. • Injection techniques targeting the extraforaminal or juxta-articular facet space are safer than transforaminal injections and effectively relieve pain in patients with cervical radiculopathy.

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases.

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body.

Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage. VIDEO ABSTRACT.

Is computer-assisted aminoglycoside dosing managed by a pharmacist a safety tool of pharmacotherapy?

This pilot prospective study verified the hypothesis that use of computer-assisted therapeutic drug monitoring of aminoglycosides by pharmacists leads to better safety therapeutic outcomes and cost avoidance than only concentration measurement and dose adjustments based on a physician's experience. Two groups of patients were enrolled according to the technique of monitoring. Patients (Group 1, n=52) underwent monitoring by a pharmacist using pharmacokinetic software. In a control group (Group 2, n=11), plasma levels were measured but not interpreted by the pharmacist, only by physicians. No statistically significant differences were found between the groups in factors influenced by therapy. However, the results are not statistically significant but a comparison of the groups showed a clear trend towards safety and cost avoidance, thus supporting therapeutic drug monitoring. Safety limits were achieved in 76 % and 63 % of cases in Groups 1 and 2, respectively. More patients achieved both concentrations (peak and trough) with falling eGFR in Group 1. In present pilot study, the pharmacist improved the care of patients on aminoglycoside therapy. A larger study is needed to demonstrate statistically significantly improved safety and cost avoidance of aminoglycoside therapy monitoring by the pharmacist using pharmacokinetic software.

Optimal EPO dosing in hemodialysis patients using a non-linear model predictive control approach.

Anemia management with erythropoiesis stimulating agents is a challenging task in hemodialysis patients since their response to treatment varies highly. In general, it is difficult to achieve and maintain the predefined hemoglobin (Hgb) target levels in clinical practice. The aim of this study is to develop a fully personalizable controller scheme to stabilize Hgb levels within a narrow target window while keeping drug doses low to mitigate side effects. First in-silico results of this framework are presented in this paper. Based on a model of erythropoiesis we formulate a non-linear model predictive control (NMPC) algorithm for the individualized optimization of epoetin alfa (EPO) doses. Previous to this work, model parameters were estimated for individual patients using clinical data. The optimal control problem is formulated for a continuous drug administration. This is currently a hypothetical form of drug administration for EPO as it would require a programmable EPO pump similar to insulin pumps used to treat patients with diabetes mellitus. In each step of the NMPC method the open-loop problem is solved with a projected quasi-Newton method. The controller is successfully tested in-silico on several patient parameter sets. An appropriate control is feasible in the tested patients under the assumption that the controlled quantity is measured regularly and that continuous EPO administration is adjusted on a daily, weekly or monthly basis. Further, the controller satisfactorily handles the following challenging problems in simulations: bleedings, missed administrations and dosing errors.

Designing combination therapies with modeling chaperoned machine learning.

Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model's predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a "two-wave killing" temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy.

Comparison of Routine and Computer-Guided Glucose Management for Glycemic Control in Critically Ill Patients.

BACKGROUND: Glycemic control is crucial for reducing morbidity and mortality in critically ill patients. A standardized approach to glycemic control using a computer-guided protocol may help maintain blood glucose level within a target range and prevent human-induced medical errors. OBJECTIVE: To determine the effectiveness of a computer-guided glucose management protocol for glycemic control in intensive care patients. METHODS: This controlled, open-label implementation study involved 66 intensive care patients: 33 in the intervention group and 33 in the control group. The blood glucose level target range was established as 120 to 180 mg/dL. The control group received the clinic's routine glycemic monitoring approach, and the intervention group received monitoring using newly developed glycemic control software. At the end of the study, nurse perceptions and satisfaction were determined using a questionnaire. RESULTS: The rates of hyperglycemia and hypoglycemia were lower and the blood glucose level was more successfully maintained in the target range in the intervention group than in the control group (P < .001). The time to achieve the target range was shorter and less insulin was used in the intervention group than in the control group (P < .05). Nurses reported higher levels of satisfaction with the computerized protocol, which they found to be more effective and reliable than routine clinical practice. CONCLUSIONS: The computerized protocol was more effective than routine clinical practice in achieving glycemic control. It was also associated with higher nurse satisfaction levels.

Computer-Assisted Propofol Sedation for Esophagogastroduodenoscopy Is Effective, Efficient, and Safe.

BACKGROUND AND AIMS: Computer-assisted propofol sedation (CAPS) allows non-anesthesiologists to administer propofol for gastrointestinal procedures in relatively healthy patients. As the first US medical center to adopt CAPS technology for routine clinical use, we report our 1-year experience with CAPS for esophagogastroduodenoscopy (EGD). METHODS: Between September 2014 and August 2015, 926 outpatients underwent elective EGDs with CAPS at our center. All EGDs were performed by 1 of 17 gastroenterologists certified in the use of CAPS. Procedural success rates, procedure times, and recovery times were compared against corresponding historical controls done with midazolam and fentanyl sedation from September 2013 to August 2014. Adverse events in CAPS patients were recorded. RESULTS: The mean age of the CAPS cohort was 56.7 years (45% male); 16.2% of the EGDs were for variceal screening or Barrett's surveillance and 83.8% for symptoms. The procedural success rates were similar to that of historical controls (99.0% vs. 99.3%; p = 0.532); procedure times were also similar (6.6 vs. 7.4 min; p = 0.280), but recovery time was markedly shorter (31.7 vs. 52.4 min; p < 0.001). There were 11 (1.2%) cases of mild transient oxygen desaturation (< 90%), 15 (1.6%) cases of marked agitation due to undersedation, and 1 case of asymptomatic hypotension. In addition, there were six (0.6%) patients with more pronounced desaturation episodes that required brief (< 1 min) mask ventilation. There were no other serious adverse events. CONCLUSIONS: CAPS appears to be a safe, effective, and efficient means of providing sedation for EGD in healthy patients. Recovery times were much shorter than historical controls.

Fully closed-loop insulin delivery improves glucose control of inpatients with type 2 diabetes receiving hemodialysis.

Inpatient diabetes management of those on hemodialysis poses a major challenge. In a post hoc analysis of a randomized controlled clinical trial, we compared the efficacy of fully automated closed-loop insulin delivery vs. usual care in patients undergoing hemodialysis while in hospital. Compared to control patients receiving conventional subcutaneous insulin therapy, those patients receiving closed-loop insulin delivery significantly increased the proportion of time when a continuous glucose monitor was in the target range of 5.6-10.0 mmol/l by 37.6 percent without increasing the risk of hypoglycemia. Thus, closed-loop insulin delivery offers a novel way to achieve effective and safe glucose control in this vulnerable patient population.

Serial Cardiac FDG-PET for the Diagnosis and Therapeutic Guidance of Patients With Cardiac Sarcoidosis.

BACKGROUND: Cardiac fluorodeoxyglucose positron-emission tomography (FDG-PET) has emerged as a standard imaging modality for the diagnosis of cardiac sarcoidosis (CS); however, there is a scarcity of data on the use of serial FDG-PET to guide immunosuppressive therapy. The aim of this work was to report our experience using serial FDG-PET for the diagnosis and management of patients with CS, focusing on its utility in ongoing immunosuppression management. METHODS AND RESULTS: We studied consecutive patients with CS managed at Stanford University from 2010 to 2017. We evaluated our experience using FDG-PET for diagnosis and guidance of immunosuppressive therapy titration in CS. Among 34 patients diagnosed with CS, 16 (47%), 12 (35%) and 14(41%) presented with heart block, heart failure, and ventricular arrhythmias, respectively. FDG-PET proved beneficial in the initial diagnosis in 21 patients (62%). A total of 128 FDG-PET scans were performed (median 3 per patient). Ninety-four FDG-PET scans (73%) resulted in a change in therapy, with 42FDG-PET scans (33%) instrumental for tapering prednisone. Among patients who were initiated on prednisone, the mean dose of prednisone at 1 year was 9.5mg/d. Over a median follow-up of 2.3years, 48% of patients were successfully weaned from prednisone completely, and 20% were weaned to a maintenance dosage of 5-10mg/d. During the follow-up period, transplant-free survival was 88%. CONCLUSIONS: The use of serial cardiac FDG-PET for the diagnosis and management of CS was critical for guiding immunosuppression management and resulted in low chronic steroid doses and good disease control within 1 year of diagnosis.

Computerized hybrid decision-making system for hormone replacement therapy in menopausal women.

BACKGROUND: The diversity of the results of different hormone replacement therapy (HRT) protocols and the fuzziness of the conclusions have caused problems in routine clinical practice. OBJECTIVE: To develop an intelligent decision-making system for HRT specifically is appropriate as we use the abbrevation HRT in the background section in menopausal women in order to assist physicians. METHODS: This study consisted of 179 peri- and post-menopausal patients who were admitted to Hacettepe University Hospital (between 1996 and 2001) with various menopausal complaints. Database variables used in this study were age, height, weight, menopause duration, clinical condition, HRT duration, and the laboratory test results. Our newly developed multiple-centered fuzzy clustering (MCFC) algorithm was applied to the medical data set to differentiate patient groups. Finally, a hybrid intelligent decision-making system was developed by combining knowledge-based algorithms and the MCFC algorithm results. RESULTS: We have used Fuzzy C-means, K-means, Hard C-means, similarity based clustering, and MCFC algorithms on the medical data set and have determined that the MCFC algorithm is the most appropriate algorithm for our medical model. We have defined 5 clusters and 16 cluster centers. A diagnostic phrase was assigned to each cluster center by the physician and these clusters together with knowledge-based algorithms were used for the decision-making system. CONCLUSIONS: We have developed a computerized hybrid decision-making system recommending HRT to peri- and post-menopausal women in order to assist and protect physicians.

On medical application of neural networks trained with various types of data.

Neural networks have garnered attention over the past few years. A neural network is a typical model of machine learning that is used to identify visual patterns. Neural networks are used to solve a wide variety of problems, including image recognition problems and time series prediction problems. In addition, neural networks have been applied to medicine over the past few years. This paper classifies the ways in which neural networks have been applied to medicine based on the type of data used to train those networks. Applications of neural networks to medicine can be categorized two types: automated diagnosis and physician aids. Considering the number of patients per physician, neural networks could be used to diagnose diseases related to the vascular system, heart, brain, spinal column, head, neck, and tumors/cancer in three fields: vascular and interventional radiology, interventional cardiology, and neuroradiology. Lastly, this paper also considers areas of medicine where neural networks can be effectively applied in the future.

A Quality Improvement Bundle Including Pay for Performance for the Standardization of Order Set Use in Moderate Asthma.

OBJECTIVE: In order to standardize use of our hospital's computerized asthma order set, which was developed based on an asthma clinical practice guideline, for moderately ill children presenting for care of asthma, we developed a quality improvement bundle, including a time-limited pay-for-performance component, for pediatric emergency department and pediatric urgent care faculty members. METHODS: Following baseline measurement, we used a run-in period for education, feedback, and improvement of the asthma order set. Then, faculty members earned 0.1% of salary during each of 10 successive months (evaluation period) in which the asthma order set was used in managing 90% or more of eligible patients. RESULTS: At baseline, the asthma order set was used in managing 60.5% of eligible patients. Order set use rose sharply during the run-in period. During the 10-month evaluation period, use of the asthma order set was significantly above baseline, with a mean of 91.6%; faculty earned pay-for-performance bonuses during 8 of 10 possible months. Following completion of the evaluation period, asthma order set use remained high. CONCLUSIONS: A quality improvement bundle, including a time-limited pay-for-performance component, was associated with a sustained increase in the use of a computerized asthma order set for managing moderately ill asthmatic children.

Newer propofol, ketamine, and etomidate derivatives and delivery systems relevant to anesthesia practice.

Drug discovery is the cornerstone of developments in the field of anesthesia. Each year, new drugs enter the market and possibly change clinical practice. The development of new anesthetics can be divided into two groups. One strategy is the discovery of a new type of drug with unique molecular structure, better clinical properties, and lesser side effects than the already existing drugs. Another strategy is changing the molecular structure of current clinically available anesthetics to create new drugs with better pharmacokinetic and pharmacodynamic properties. In this review, we describe the current developments of frequently used anesthetics, namely, propofol, etomidate, and ketamine. Alfaxalone is an old anesthetic with favorable properties such as hemodynamic and respiratory stability but lacks appropriate vehicle. New formulations with recently developed solvents together with this old anesthetic are created. Next to drug development, there is also a constant search for better delivery systems for the already available anesthetics. Following open-loop systems like TIVA, new closed-loop systems have entered the market. We also discuss about SEDASYS®-Computer-Assisted Personalized Sedation System, an automatic closed-loop delivery system that provides propofol sedation for endoscopic procedures.

Computerised interventions designed to reduce potentially inappropriate prescribing in hospitalised older adults: a systematic review and meta-analysis.

Background: computerised interventions have been suggested as an effective strategy to reduce potentially inappropriate prescribing (PIP) for hospitalised older adults. This systematic review and meta-analysis examined the evidence for efficacy of computerised interventions designed to reduce PIP in this patient group. Methods: an electronic literature search was conducted using eight databases up to October 2017. Included studies were controlled trials of computerised interventions aiming to reduce PIP in hospitalised older adults (≥65 years). Risk of bias was assessed using Cochrane's Effective Practice and Organisation of Care criteria. Results: of 653 records identified, eight studies were included-two randomised controlled trials, two interrupted time series analysis studies and four controlled before-after studies. Included studies were mostly at a low risk of bias. Overall, seven studies showed either a statistically significant reduction in the proportion of patients prescribed a potentially inappropriate medicine (PIM) (absolute risk reduction {ARR} 1.3-30.1%), or in PIMs ordered (ARR 2-5.9%). However, there is insufficient evidence thus far to suggest that these interventions can routinely improve patient-related outcomes. It was only possible to include three studies in the meta-analysis-which demonstrated that intervention patients were less likely to be prescribed a PIM (odds ratio 0.6; 95% CI 0.38, 0.93). No computerised intervention targeting potential prescribing omissions (PPOs) was identified. Conclusions: this systematic review concludes that computerised interventions are capable of statistically significantly reducing PIMs in hospitalised older adults. Future interventions should strive to target both PIMs and PPOs, ideally demonstrating both cost-effectiveness data and clinically significant improvements in patient-related outcomes.

Polimedication: applicability of a computer tool to reduce polypharmacy in nursing homes.

ABSTRACTBackground:The risks of polypharmacy can be far greater than the benefits, especially in the elderly. Comorbidity makes polypharmacy very prevalent in this population; thus, increasing the occurrence of adverse effects. To solve this problem, the most common strategy is to use lists of potentially inappropriate medications. However, this strategy is time consuming. METHODS: In order to minimize the expenditure of time, our group devised a pilot computer tool (Polimedication) that automatically processes lists of medication providing the corresponding Screening Tool of Older Persons' potentially inappropriate Prescriptions alerts and facilitating standardized reports. The drug lists for 115 residents in Santa Marta Nursing Home (Fundación San Rosendo, Ourense, Spain) were processed. RESULTS: The program detected 10.04 alerts/patient, of which 74.29% were not repeated. After reviewing these alerts, 12.12% of the total (1.30 alerts/patient) were considered relevant. The largest number of alerts (41.48%) involved neuroleptic drugs. Finally, the patient's family physician or psychiatrist accepted the alert and made medication changes in 62.86% of the relevant alerts. The largest number of changes (38.64%) also involved neuroleptic drugs. The mean time spent in the generation and review of the warnings was 6.26 minute/patient. Total changes represented a saving of 32.77 € per resident/year in medication. CONCLUSIONS: The application of Polimedication tool detected a high proportion of potentially inappropriate prescriptions in institutionalized elderly patients. The use of the computerized tool achieved significant savings in pharmaceutical expenditure, as well as a reduction in the time taken for medication review.

Improving medication-related clinical decision support.

PURPOSE: Current uses of medication-related clinical decision support (CDS) and recommendations for improving these systems are reviewed. SUMMARY: Using a systematic approach, articles published from 2007 through 2014 were identified in MEDLINE and EMBASE using MeSH terms and keywords relating to the 5 basic medication-related CDS functionalities. A total of 156 full-text articles and 28 conference abstracts were reviewed across each of the 5 areas: drug-drug interaction (DDI) checks (n = 78), drug allergy checks (n = 20), drug dose support (n = 55), drug duplication checks (n = 11), and drug formulary support (n = 20). The success of medication-related CDS depends on users finding the alerts valuable and acting on the information received. Improving alert specificity and sensitivity is important for all domains. Tiering is important for improving the acceptance of DDI alerts. The ability to perform appropriate cross-sensitivity checks is key to producing appropriate drug allergy checks. Drug dosage alerts should be individualized and deliver practical recommendations. How the system is configured to identify certain drug duplications is important to prevent possible patient toxicity. Accurate knowledge databases are needed to produce relevant drug formulary alerts and encourage formulary adherence. Medication-related CDS is still relatively immature in some organizations and has substantial room for improvement. For example, decision support should consider more patient-specific factors, human factors principles should always be considered, and alert specificity must be improved in order to reduce alert fatigue. CONCLUSION: Standardization, integration of patient-specific parameters, and consideration of human factors design principles are central to realizing the potential benefits of medication-related CDS.

Developing an electronic system to manage and track emergency medications.

PURPOSE: The development of a Web-based program to track and manage emergency medications with radio frequency identification (RFID) is described. SUMMARY: At the Cleveland Clinic, medication kit restocking records and dispense locations were historically documented using a paper record-keeping system. The Cleveland Clinic investigated options to replace the paper-based tracking logs with a Web-based program that could track the real-time location and inventory of emergency medication kits. Vendor collaboration with a board of pharmacy (BOP) compliance inspector and pharmacy personnel resulted in the creation of a dual barcoding system using medication and pocket labels. The Web-based program was integrated with a Cleveland Clinic-developed asset tracking system using active RFID tags to give the real-time location of the medication kit. The Web-based program and the asset tracking system allowed identification of kits nearing expiration or containing recalled medications. Conversion from a paper-based system to a Web-based program began in October 2013. After 119 days, data were evaluated to assess the success of the conversion. Pharmacists spent an average of 27 minutes per day approving medication kits during the postimplementation period versus 102 minutes daily using the paper-based system, representing a 74% decrease in pharmacist time spent on this task. Prospective reports are generated monthly to allow the manager to assess the expected workload and adjust staffing for the next month. CONCLUSION: Implementation of a BOP-approved Web-based system for managing and tracking emergency medications with RFID integration decreased pharmacist review time, minimized compliance risk, and increased access to real-time data.