Medication safety and knowledge-based functions: a stepwise approach against information overload

Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review

BACKGROUND

Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals.

OBJECTIVE

This scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described.

METHODS

A total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the "Active Implementation Framework stages": exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies.

RESULTS

A total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework.

CONCLUSIONS

Models and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs.

Dealing with information overload: a comprehensive review

Information overload is a problem that is being exacerbated by the ongoing digitalization of the world of work and the growing use of information and communication technologies. Therefore, the aim of this systematic literature review is to provide an insight into existing measures for prevention and intervention related to information overload. The methodological approach of the systematic review is based on the PRISMA standards. A keyword search in three interdisciplinary scientific databases and other more practice-oriented databases resulted in the identification of 87 studies, field reports, and conceptual papers that were included in the review. The results show that a considerable number of papers have been published on interventions on the behavioral prevention level. At the level of structural prevention, there are also many proposals on how to design work to reduce information overload. A further distinction can be made between work design approaches at the level of information and communication technology and at the level of teamwork and organizational regulations. Although the identified studies cover a wide range of possible interventions and design approaches to address information overload, the strength of the evidence from these studies is mixed.

Using a Machine Learning System to Identify and Prevent Medication Prescribing Errors: A Clinical and Cost Analysis Evaluation

BACKGROUND

Clinical decision support (CDS) alerting tools can identify and reduce medication errors. However, they are typically rule-based and can identify only the errors previously programmed into their alerting logic. Machine learning holds promise for improving medication error detection and reducing costs associated with adverse events. This study evaluates the ability of a machine learning system (MedAware) to generate clinically valid alerts and estimates the cost savings associated with potentially prevented adverse events.

METHODS

Alerts were generated retrospectively by the MedAware system on outpatient data from two academic medical centers between 2009 and 2013. MedAware alerts were compared to alerts in an existing CDS system. A random sample of 300 alerts was selected for medical record review. Frequency and severity of potential outcomes of alerted medication errors of medium and high clinical value were estimated, along with associated health care costs of these potentially prevented adverse events.

RESULTS

A total of 10,668 alerts were generated. Overall, 68.2% of MedAware alerts would not have been generated by the existing CDS system. Ninety-two percent of a random sample of the chart-reviewed alerts were accurate based on structured data available in the record, and 79.7% were clinically valid. Estimated cost of adverse events potentially prevented in an outpatient setting was more than $60 per drug alert and $1.3 million when extrapolating study findings to the full patient population.

CONCLUSION

A machine learning system identified clinically valid medication error alerts that might otherwise be missed with existing CDS systems. Estimates show potential for cost savings associated with potentially prevented adverse events.

[Diagnostic procedures and diagnostic strategy for lower urinary tract symptoms/benign prostatic hyperplasia : An overview]

BACKGROUND

Lower urinary tract symptoms due to benign prostatic hyperplasia (LUTS/BPH) is the most common condition affecting the lower urinary tract of men. Evidence-based assessment is the basis for an ideal treatment approach.

OBJECTIVES

To provide an overview of the current status of diagnostic measures for LUTS/BPH.

MATERIALS AND METHODS

Descriptive review of the literature on the diagnosis of LUTS/BPH.

RESULTS

A medical history inquiring about LUTS/BPH symptoms and burden as well as a standardized and validated symptom questionnaire such as the International Prostate Symptom Score (IPSS) are the basis of the assessment. A physical examination including a rectal exam and the ultrasonography of the lower and upper urinary tract are also part of the basic diagnostic workup. Prostate size is ideally measured by transrectal ultrasound. Serum prostate-specific antigen measurement may help to estimate the prostate size and the risk fo progression. It can also be helpful in the detection of prostate cancer. Urine dipstick or sediment is used to exclude urinary tract infection, hematuria, or glucosuria. Voiding dysfunction can be detected by uroflowmetry. In addition to the aforementioned examinations, further tests such as frequency-voiding charts, multichannel urodynamic evaluation, measurement of detrusor wall thickness and X‑ray imaging of the upper urinary tract as well as a cystoscopy may be offered if needed.

CONCLUSIONS

Diagnostics of LUTS/BPH consist of basic exams as well as optional exams and can be used to assess the progression risk, to identify complications and to offer the ideal treatment.

Point-of-care Cognitive Support Technology in Emergency Departments: A Scoping Review of Technology Acceptance by Clinicians

OBJECTIVE

Cognitive support technologies that support clinical decisions and practices in the emergency department (ED) have the potential to optimize patient care. However, limited uptake by clinicians can prevent successful implementation. A better understanding of acceptance of these technologies from the clinician perspective is needed. We conducted a scoping review to synthesize diverse, emerging evidence on clinicians' acceptance of point-of-care (POC) cognitive support technology in the ED.

METHOD

We systematically searched 10 electronic databases and gray literature published from January 2006 to December 2016. Studies of any design assessing an ED-based POC cognitive support technology were considered eligible for inclusion. Studies were required to report outcome data for technology acceptance. Two reviewers independently screened studies for relevance and quality. Study quality was assessed using the Mixed-Methods Appraisal Tool. A descriptive analysis of the features of POC cognitive support technology for each study is presented, illustrating trends in technology development and evaluation. A thematic analysis of clinician, technical, patient, and organizational factors associated with technology acceptance is also presented.

RESULTS

Of the 1,563 references screened for eligibility, 24 met the inclusion criteria and were included in the review. Most studies were published from 2011 onward (88%), scored high for methodologic quality (79%), and examined POC technologies that were novel and newly introduced into the study setting (63%). Physician use of POC technology was the most commonly studied (67%). Technology acceptance was frequently conceptualized and measured by factors related to clinician attitudes and beliefs. Experience with the technology, intention to use, and actual use were also more common outcome measures of technology acceptance. Across studies, perceived usefulness was the most noteworthy factor impacting technology acceptance, and clinicians generally had positive perceptions of the use of POC cognitive support technology in the ED. However, the actual use of POC cognitive support technology reported by clinicians was low-use, by proportion of patient cases, ranged from 30% to 59%. Of the 24 studies, only two studies investigated acceptance of POC cognitive support technology currently implemented in the ED, offering "real-world" clinical practice data. All other studies focused on acceptance of novel technologies. Technical aspects such as an unfriendly user interface, presentation of redundant or ambiguous information, and required user effort had a negative impact on acceptance. Patient expectations were also found to have a negative impact, while patient safety implications had a positive impact. Institutional support was also reported to impact technology acceptance.

CONCLUSIONS

Findings from this scoping review suggest that while ED clinicians acknowledge the utility and value of using POC cognitive support technology, actual use of such technology can be low. Further, few studies have evaluated the acceptance and use of POC technologies in routine care. Prospective studies that evaluate how ED clinicians appraise and consider POC technology use in clinical practice are now needed with diverse clinician samples. While this review identified multiple factors contributing to technology acceptance, determining how clinician, technical, patient, and organizational factors mediate or moderate acceptance should also be a priority.

Variation in high-priority drug-drug interaction alerts across institutions and electronic health records

Objective: The United States Office of the National Coordinator for Health Information Technology sponsored the development of a “high-priority” list of drug-drug interactions (DDIs) to be used for clinical decision support. We assessed current adoption of this list and current alerting practice for these DDIs with regard to alert implementation (presence or absence of an alert) and display (alert appearance as interruptive or passive).

Materials and methods: We conducted evaluations of electronic health records (EHRs) at a convenience sample of health care organizations across the United States using a standardized testing protocol with simulated orders.

Results: Evaluations of 19 systems were conducted at 13 sites using 14 different EHRs. Across systems, 69% of the high-priority DDI pairs produced alerts. Implementation and display of the DDI alerts tested varied between systems, even when the same EHR vendor was used. Across the drug pairs evaluated, implementation and display of DDI alerts differed, ranging from 27% (4/15) to 93% (14/15) implementation.

Discussion: Currently, there is no standard of care covering which DDI alerts to implement or how to display them to providers. Opportunities to improve DDI alerting include using differential displays based on DDI severity, establishing improved lists of clinically significant DDIs, and thoroughly reviewing organizational implementation decisions regarding DDIs.

Conclusion: DDI alerting is clinically important but not standardized. There is significant room for improvement and standardization around evidence-based DDIs.

Data Requirements for the Correct Identification of Medication Errors and Adverse Drug Events in Patients Presenting at an Emergency Department

BACKGROUND

Adverse drug events (ADE) involving or not involving medication errors (ME) are common, but frequently remain undetected as such. Presently, the majority of available clinical decision support systems (CDSS) relies mostly on coded medication data for the generation of drug alerts. It was the aim of our study to identify the key types of data required for the adequate detection and classification of adverse drug events (ADE) and medication errors (ME) in patients presenting at an emergency department (ED).

METHODS

As part of a prospective study, ADE and ME were identified in 1510 patients presenting at the ED of an university teaching hospital by an interdisciplinary panel of specialists in emergency medicine, clinical pharmacology and pharmacy. For each ADE and ME the required different clinical data sources (i.e. information items such as acute clinical symptoms, underlying diseases, laboratory values or ECG) for the detection and correct classification were evaluated.

RESULTS

Of all 739 ADE identified 387 (52.4%), 298 (40.3%), 54 (7.3%), respectively, required one, two, or three, more information items to be detected and correctly classified. Only 68 (10.2%) of the ME were simple drug-drug interactions that could be identified based on medication data alone while 381 (57.5%), 181 (27.3%) and 33 (5.0%) of the ME required one, two or three additional information items, respectively, for detection and clinical classification.

CONCLUSIONS

Only 10% of all ME observed in emergency patients could be identified on the basis of medication data alone. Focusing electronic decisions support on more easily available drug data alone may lead to an under-detection of clinically relevant ADE and ME.

User-centered design improves the usability of drug-drug interaction alerts: Experimental comparison of interfaces

Clinical Decision Support Systems can alert health professionals about drug interactions when they prescribe medications. The Hospital Italiano de Buenos Aires in Argentina developed an electronic health record with drug-drug interaction alerts, using traditional software engineering techniques and requirements. Despite enhancing the drug-drug interaction knowledge database, the alert override rate of this system was very high. We redesigned the alert system using user-centered design (UCD) and participatory design techniques to enhance the drug-drug interaction alert interface. This paper describes the methodology of our UCD. We used crossover method with realistic, clinical vignettes to compare usability of the standard and new software versions in terms of efficiency, effectiveness, and user satisfaction. Our study showed that, compared to the traditional alert system, the UCD alert system was more efficient (alerts faster resolution), more effective (tasks completed with fewer errors), and more satisfying. These results indicate that UCD techniques that follow ISO 9241-210 can generate more usable alerts than traditional design.

Effects of Shared Electronic Health Record Systems on Drug-Drug Interaction and Duplication Warning Detection

Shared electronic health records (EHRs) systems can offer a complete medication overview of the prescriptions of different health care providers. We use health claims data of more than 1 million Austrians in 2006 and 2007 with 27 million prescriptions to estimate the effect of shared EHR systems on drug-drug interaction (DDI) and duplication warnings detection and prevention. The Austria Codex and the ATC/DDD information were used as a knowledge base to detect possible DDIs. DDIs are categorized as severe, moderate, and minor interactions. In comparison to the current situation where only DDIs between drugs issued by a single health care provider can be checked, the number of warnings increases significantly if all drugs of a patient are checked: severe DDI warnings would be detected for 20% more persons, and the number of severe DDI warnings and duplication warnings would increase by 17%. We show that not only do shared EHR systems help to detect more patients with warnings but DDIs are also detected more frequently. Patient safety can be increased using shared EHR systems.

Design and evaluation of a software for the objective and easy-to-read presentation of new drug properties to physicians

BACKGROUND

When new pharmaceutical products appear on the market, physicians need to know whether they are likely to be useful in their practices. Physicians currently obtain most of their information about the market release and properties of new drugs from pharmaceutical industry representatives. However, the official information contained in the summary of product characteristics (SPCs) and evaluation reports from health agencies, provide a more complete view of the potential value of new drugs, although they can be long and difficult to read. The main objective of this work was to design a prototype computer program to facilitate the objective appraisal of the potential value of a new pharmaceutical product by physicians. This prototype is based on the modeling of pharmaceutical innovations described in a previous paper.

METHODS

The interface was designed to allow physicians to develop a rapid understanding of the value of a new drug for their practices. We selected five new pharmaceutical products, to illustrate the function of this prototype. We considered only the texts supplied by national or international drug agencies at the time of market release. The perceived usability of the prototype was evaluated qualitatively, except for the System Usability Scale (SUS) score evaluation, by 10 physicians differing in age and medical background.

RESULTS

The display is based on the various axes of the conceptual model of pharmaceutical innovations. The user can select three levels of detail when consulting this information (highly synthetic, synthetic and detailed). Tables provide a comparison of the properties of the new pharmaceutical product with those of existing drugs, if available for the same indication, in terms of efficacy, safety and ease of use. The interface was highly appreciated by evaluators, who found it easy to understand and suggested no other additions of important, internationally valid information. The mean System Usability Scale score for the 10 physicians was 82, corresponding to a "good" user interface.

CONCLUSIONS

This work led us to propose the selection, grouping, and mode of presentation for various types of knowledge on pharmaceutical innovations in a way that was appreciated by evaluators. It provides physicians with readily accessible objective information about new drugs.

Estimation of severe drug-drug interaction warnings by medical specialist groups for Austrian nationwide eMedication

OBJECTIVE

The objective of this study is to estimate the amount of severe drug-drug interaction warnings per medical specialist group triggered by prescribed drugs of a patient before and after the introduction of a nationwide eMedication system in Austria planned for 2015.

METHODS

The estimations of interaction warnings are based on patients' prescriptions of a single health care professional per patient, as well as all patients' prescriptions from all visited health care professionals. We used a research database of the Main Association of Austrian Social Security Organizations that contains health claims data of the years 2006 and 2007.

RESULTS

The study cohort consists of about 1 million patients, with 26.4 million prescribed drugs from about 3,400 different health care professionals. The estimation of interaction warnings show a heterogeneous pattern of severe drug-drug-interaction warnings across medical specialist groups.

CONCLUSION

During an eMedication implementation it must be taken into consideration that different medical specialist groups require customized support.

Application of the Pareto principle to identify and address drug-therapy safety issues

PURPOSE

Adverse drug events (ADE) and medication errors (ME) are common causes of morbidity in patients presenting at emergency departments (ED). Recognition of ADE as being drug related and prevention of ME are key to enhancing pharmacotherapy safety in ED. We assessed the applicability of the Pareto principle (~80 % of effects result from 20 % of causes) to address locally relevant problems of drug therapy.

METHODS

In 752 cases consecutively admitted to the nontraumatic ED of a major regional hospital, ADE, ME, contributing drugs, preventability, and detection rates of ADE by ED staff were investigated. Symptoms, errors, and drugs were sorted by frequency in order to apply the Pareto principle.

RESULTS

In total, 242 ADE were observed, and 148 (61.2 %) were assessed as preventable. ADE contributed to 110 inpatient hospitalizations. The ten most frequent symptoms were causally involved in 88 (80.0 %) inpatient hospitalizations. Only 45 (18.6 %) ADE were recognized as drug-related problems until discharge from the ED. A limited set of 33 drugs accounted for 184 (76.0 %) ADE; ME contributed to 57 ADE. Frequency-based listing of ADE, ME, and drugs involved allowed identification of the most relevant problems and development of easily to implement safety measures, such as wall and pocket charts.

CONCLUSIONS

The Pareto principle provides a method for identifying the locally most relevant ADE, ME, and involved drugs. This permits subsequent development of interventions to increase patient safety in the ED admission process that best suit local needs.

Comparative evaluation of different medication safety measures for the emergency department: physicians' usage and acceptance of training, poster, checklist and computerized decision support

Background

Although usage and acceptance are important factors for a successful implementation of clinical decision support systems for medication, most studies only concentrate on their design and outcome. Our objective was to comparatively investigate a set of traditional medication safety measures such as medication safety training for physicians, paper-based posters and checklists concerning potential medication problems versus the additional benefit of a computer-assisted medication check. We concentrated on usage, acceptance and suitability of such interventions in a busy emergency department (ED) of a 749 bed acute tertiary care hospital.

Methods

A retrospective, qualitative evaluation study was conducted using a field observation and a questionnaire-based survey. Six physicians were observed while treating 20 patient cases; the questionnaire, based on the Technology Acceptance Model 2 (TAM2), has been answered by nine ED physicians.

Results

During field observations, we did not observe direct use of any of the implemented interventions for medication safety (paper-based and electronic). Questionnaire results indicated that the electronic medication safety check was the most frequently used intervention, followed by checklist and posters. However, despite their positive attitude, physicians most often stated that they use the interventions in only up to ten percent for subjectively “critical” orders. Main reasons behind the low usage were deficits in ease-of-use and fit to the workflow. The intention to use the interventions was rather high after overcoming these barriers.

Conclusions

Methodologically, the study contributes to Technology Acceptance Model (TAM) research in an ED setting and confirms TAM2 as a helpful diagnostic tool in identifying barriers for a successful implementation of medication safety interventions. In our case, identified barriers explaining the low utilization of the implemented medication safety interventions - despite their positive reception - include deficits in accessibility, briefing for the physicians about the interventions, ease-of-use and compatibility to the working environment.