Quality indicators for appropriate in-hospital pharmacotherapeutic stewardship: An international modified Delphi study

AIMS

In-hospital prescribing errors may result in patient harm, such as prolonged hospitalisation and hospital (re)admission, and may be an emotional burden for the prescribers and healthcare professionals involved. Despite efforts, in-hospital prescribing errors and related harm still occur, necessitating an innovative approach. We therefore propose a novel approach, in-hospital pharmacotherapeutic stewardship (IPS). The aim of this study was to reach consensus on a set of quality indicators (QIs) as a basis for IPS.

METHODS

A three-round modified Delphi procedure was performed. Potential QIs were retrieved from two systematic searches of the literature, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. In two written questionnaires and a focus meeting (held between the written questionnaire rounds), potential QIs were appraised by an international, multidisciplinary expert panel composed of members of the European Association for Clinical Pharmacology and Therapeutics (EACPT).

RESULTS

The expert panel rated 59 QIs and four general statements, of which 35 QIs were accepted with consensus rates ranging between 79% and 97%. These QIs describe the activities of an IPS programme, the team delivering IPS, the patients eligible for the programme and the outcome measures that should be used to evaluate the care delivered.

CONCLUSIONS

A framework of 35 QIs for an IPS programme was systematically developed. These QIs can guide hospitals in setting up a pharmacotherapeutic stewardship programme to reduce in-hospital prescribing errors and improve in-hospital medication safety.

The effect of electronic medical records on medication errors, workload, and medical information availability among qualified nurses in Israel- a cross sectional study

BACKGROUND

Errors in medication administration by qualified nursing staff in hospitals are a significant risk factor for patient safety. In recent decades, electronic medical records (EMR) systems have been implemented in hospitals, and it has been claimed that they contribute to reducing such errors. However, systematic research on the subject in Israel is scarce. This study examines the position of the qualified nursing staff regarding the impact of electronic medical records systems on factors related to patient safety, including errors in medication administration, workload, and availability of medical information.

METHODS

This cross-sectional study examines three main variables: Medication errors, workload, and medical information availability, comparing two periods- before and after EMR implementation based on self-reports. A final sample of 591 Israeli nurses was recruited using online private social media groups to complete an online structured questionnaire. The questionnaires included items assessing workload (using the Expanding Nursing Stress Scale), medical information availability (the Carrington-Gephart Unintended Consequences of Electronic Health Record Questionnaire), and medical errors (the Medical Error Checklists). Items were assessed twice, once for the period before the introduction of electronic records and once after. In addition, participants answered open-ended questions that were qualitatively analyzed.

RESULTS

Nurses perceive the EMR as reducing the extent of errors in drug administration (mean difference = -0.92 ± 0.90SD, p < 0.001), as well as the workload (mean difference = -0.83 ± 1.03SD, p < 0.001) by ∼ 30% on average, each. Concurrently, the systems are perceived to require a longer documentation time at the expense of patients' treatment time, and they may impair the availability of medical information by about 10% on average.

CONCLUSION

The results point to nurses' perceived importance of EMR systems in reducing medication errors and relieving the workload. Despite the overall positive attitudes toward EMR systems, nurses also report that they reduce information availability compared to the previous pen-and-paper approach. A need arises to improve the systems in terms of planning and adaptation to the field and provide appropriate technical and educational support to nurses using them.

Pharmacogenomics - a minor rather than major force in clinical medicine

INTRODUCTION

Pharmacogenomics (PGx) is touted as essential for the future of precision medicine. But the opportunity cost of PGx from the prescribers' perspective is rarely considered. The aim of this article is to critique PGx-guided prescribing using clinical pharmacology principles so that important cases for PGx testing are not missed by doctors responsible for therapeutic decision making.

AREAS COVERED

Three categories of PGx and their limitations are outlined - exposure PGx, response PGx, and immune-mediated safety PGx. Clinical pharmacology reasons are given for the narrow scope of PGx-guided prescribing apart from a few medical specialties. Clinical problems for doctors that may arise from PGx are then explained, including mismatch between patients' expectations of PGx testing and the benefits or answers it provides.

EXPERT OPINION

Contrary to popular opinion, PGx is unlikely to become the cornerstone of precision medicine. Sound clinical pharmacology reasons explain why PGx-guided prescribing is unnecessary for most drugs. Pharmacogenomics is important for niche areas of prescribing but has limited clinical utility more broadly. The opportunity cost of PGx-guided prescribing is currently too great for most doctors.

Virtual twin for healthcare management

Healthcare is increasingly fragmented, resulting in escalating costs, patient dissatisfaction, and sometimes adverse clinical outcomes. Strategies to decrease healthcare fragmentation are therefore attractive from payer and patient perspectives. In this commentary, a patient-centered smart phone application called Virtual Twin for Healthcare Management (VTHM) is proposed, including its organizational layout, basic functionality, and potential clinical applications. The platform features a virtual twin hub that displays the body and its health data. This is a physiologically based human model that is "virtualized" for the patient based on their unique genetic, molecular, physiological, and disease characteristics. The spokes of the system are a full service and interoperable electronic-health record, accessible to healthcare providers with permission on any device with internet access. Theoretical case studies based on real scenarios are presented to show how VTHM could potentially improve patient care and clinical efficiency. Challenges that must be overcome to turn VTHM into reality are also briefly outlined. Notably, the VTHM platform is designed to operationalize current and future precision medicine initiatives, such as access to molecular diagnostic results, pharmacogenomics-guided prescribing, and model-informed precision dosing.

Evaluation of the effectiveness of electronic prescription in reducing medical and medical errors (systematic review study)

INTRODUCTION

The use of electronic systems in prescription is considered as the final solution to overcome the many problems of the paper transcription process, especially with the outbreak of Coronavirus needs more attention than before. But despite the many advantages, its implementation faces many challenges and obstacles. Therefore, the present study was conducted to review the effectiveness of computerized physician order entry systems (CPOE) on relative risk reduction on medication error and adverse drug events (ADE).

METHOD

This study is one of the systematic review studies that was conducted in 2021. In this study, searching for keywords such as E-Electronic Prescription, Patient safety, Medication Errors prescription, Drug Interactions, orginal articles from 2000 to October-2020 in the valid databases such as ISI web of Science PubMed Embase, Scopus and search engines like google was done. The included studies were based on the main objectives of the study and based on the inclusion criteria after several stages of review and quality evaluation. In fact, the main criteria for selecting articles were studies that compared the rate of medication errors with or without assessing the associated harms (real or potential) before and after the implementation of EMS.

RESULTS

Out of 110 selected studies after initial screening, only 16 articles were selected due to their relevance. Among the final studies, there was a significant heterogeneity. Only 6 studies were of good quality. Of the 10 studies prescribing error rates, 9 reported reductions, but variable denominators prevented meta-analysis. Twelve studies provided specific examples of systemic drug errors. 5 cases reported their occurrence slightly. Out of 9 cases that analyzed the effects on drug error rate, 7 cases showed a significant relative reduction between 13 and 99%. Four of the six studies that analyzed the effects on potential ADEs showed a significant relative reduction of between 35 and 98%. Two of the four studies that analyzed the effect of ADEs showed a relative reduction of between 30 and 84%.

CONCLUSION

Finally, e-prescribing seems to reduce the risk of medication errors and ADE. However, the studies differed significantly in terms of setting, design, quality and results. More randomized controlled trials (RCTs) are needed to further improve the evidence of health informatics information.

Completeness and accuracy of adverse drug reaction documentation in electronic medical records at a tertiary care hospital in Australia

Background: A large proportion of patients presenting to hospitals have experienced a previous adverse drug reaction (ADR). Electronic medical records (EMRs) present an opportunity to accurately document ADRs and alert clinicians against inadvertent rechallenge where there is a pre-existing reaction. However, EMR systems are imperfect and rely on the accuracy of the data entered. Objective: To ascertain the completeness of ADR documentation and the accuracy of the classification of ADRs as allergy versus intolerance in the EMR at a major metropolitan hospital in Australia. Method: Cross-sectional audit of the ADR field of the EMR for a sample of patients on four different wards over 3 weeks to ascertain the completeness of ADR documentation and the accuracy of classification of ADRs. Results: Of the 264 patients assessed, 102 (38.6%) had a total of 210 ADRs documented in the EMR. Of these, 105 (50%) were considered to have complete documentation; 63/210 (30.0%) were missing a reaction description and 88/210 (41.9%) were missing severity information. For those ADRs with a reaction description (n = 147), 97 (66.0%) were considered to be appropriately classified as allergy or intolerance. Conclusion: Incomplete and inaccurate ADR documentation was common. These findings highlight a need for optimising ADR documentation to improve appropriate medication use in hospital. Implications: Improved EMR design and education of healthcare workers on the importance of complete and accurate documentation of reactions are needed to improve completeness and accuracy of ADR classification.

Automation of penicillin adverse drug reaction categorisation and risk stratification with machine learning natural language processing

BACKGROUND

The penicillin adverse drug reaction (ADR) label is common in electronic health records (EHRs). However, there is significant misclassification between allergy and intolerance within the EHR and most patients can be delabelled after an immunologic assessment. Machine learning natural language processing may be able to assist with the categorisation and risk stratification of penicillin ADRs.

OBJECTIVE

The aim of this study was to use text entered into an EHR to derive and evaluate machine learning models to classify penicillin ADRs and assess the risk of true allergy.

METHODS

Machine learning natural language processing was applied to free-text penicillin ADR data extracted from a public health system EHR. The model was developed by training on labelled dataset. ADR entries were split into training and testing datasets and used to develop and test a variety of machine learning models. These were compared to categorisation with a simple algorithm using keyword search.

RESULTS

The best performing model for the classification of penicillin ADRs as being consistent with allergy or intolerance was the artificial neural network (AUC 0.994, sensitivity 0.99, specificity 0.96). The artificial neural network also achieved the highest AUC in the classification of high- or low-risk of true allergy (AUC 0.988, sensitivity 0.99, specificity 0.99). All ADR labels were able to be classified using these machine learning models, whereas a small proportion were unclassifiable using the simple algorithm as they contained no keywords.

CONCLUSION

Machine learning natural language processing performed similarly to expert criteria in classifying and risk stratifying penicillin ADRs labels. These models outperformed simpler algorithms in their ability to interpret free-text data contained in the EHR. The automated evaluation of penicillin ADR labels may allow real-time risk stratification to facilitate delabelling and improve the specificity of prescribing alerts.

How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis

OBJECTIVE

To conduct a systematic review and meta-analysis to assess: 1) changes in medication error rates and associated patient harm following electronic medication system (EMS) implementation; and 2) evidence of system-related medication errors facilitated by the use of an EMS.

MATERIALS AND METHODS

We searched Medline, Scopus, Embase, and CINAHL for studies published between January 2005 and March 2019, comparing medication errors rates with or without assessments of related harm (actual or potential) before and after EMS implementation. EMS was defined as a computer-based system enabling the prescribing, supply, and/or administration of medicines. Study quality was assessed.

RESULTS

There was substantial heterogeneity in outcomes of the 18 included studies. Only 2 were strong quality. Meta-analysis of 5 studies reporting change in actual harm post-EMS showed no reduced risk (RR: 1.22, 95% CI: 0.18-8.38, P = .8) and meta-analysis of 3 studies reporting change in administration errors found a significant reduction in error rates (RR: 0.77, 95% CI: 0.72-0.83, P = .004). Of 10 studies of prescribing error rates, 9 reported a reduction but variable denominators precluded meta-analysis. Twelve studies provided specific examples of system-related medication errors; 5 quantified their occurrence.

DISCUSSION AND CONCLUSION

Despite the wide-scale adoption of EMS in hospitals around the world, the quality of evidence about their effectiveness in medication error and associated harm reduction is variable. Some confidence can be placed in the ability of systems to reduce prescribing error rates. However, much is still unknown about mechanisms which may be most effective in improving medication safety and design features which facilitate new error risks.

Documentation of adverse drug reactions to opioids in an electronic health record

BACKGROUND

Allergy to opioids is the second most common drug allergy label in electronic health records (EHR). Adverse drug reactions (ADR) to opioids cause significant morbidity and contribute to healthcare costs, while incorrect opioid allergy labels may unnecessarily complicate patient management.

AIMS

To examine the documentation of opioid ADR in a large-scale hospital-based EHR.

METHODS

A cross-sectional retrospective review of EHR documentation of opioid ADR at four public hospitals in South Australia was conducted. Data were extracted from all ADR entries including the reported allergen, ADR category (allergy or intolerance) and reaction details. Expert criteria were used to determine consistency of ADR categorisation as allergy or intolerance.

RESULTS

Of 86 727 unique ADR reports, there were 13 781 ADR to opioids with most being entered as allergy (n = 8913, 64.7%) rather than intolerance (n = 4868, 35.3%). The most commonly documented reactions were nausea/vomiting (n = 3912, 28%), rash (n = 647, 5%), itch (n = 642, 5%) and hallucinations (n = 527, 4%). There were 362 (3%) ADR labels of anaphylaxis. Of those ADR containing a reaction description (n = 11 868), 89% of reports entered as allergy had a reaction description that was consistent with intolerance and 8% of the entered intolerances had descriptions consistent with allergy when assessed using predefined criteria.

CONCLUSIONS

This large EHR-based study demonstrates the high rate of opioid ADR labels in EHR. The majority of these labels were for symptoms suggestive of pharmacological intolerance. Reactions consistent with true allergy were uncommon. Systematic review of ADR by a dedicated clinical service would improve the accuracy of documentation.

The impact of health information technology on prescribing errors in hospitals: a systematic review and behaviour change technique analysis

BACKGROUND

Health information technology (HIT) is known to reduce prescribing errors but may also cause new types of technology-generated errors (TGE) related to data entry, duplicate prescribing, and prescriber alert fatigue. It is unclear which component behaviour change techniques (BCTs) contribute to the effectiveness of prescribing HIT implementations and optimisation. This study aimed to (i) quantitatively assess the HIT that reduces prescribing errors in hospitals and (ii) identify the BCTs associated with effective interventions.

METHODS

Articles were identified using CINAHL, EMBASE, MEDLINE, and Web of Science to May 2020. Eligible studies compared prescribing HIT with paper-order entry and examined prescribing error rates. Studies were excluded if prescribing error rates could not be extracted, if HIT use was non-compulsory or designed for one class of medication. The Newcastle-Ottawa scale was used to assess study quality. The review was reported in accordance with the PRISMA and SWiM guidelines. Odds ratios (OR) with 95% confidence intervals (CI) were calculated across the studies. Descriptive statistics were used to summarise effect estimates. Two researchers examined studies for BCTs using a validated taxonomy. Effectiveness ratios (ER) were used to determine the potential impact of individual BCTs.

RESULTS

Thirty-five studies of variable risk of bias and limited intervention reporting were included. TGE were identified in 31 studies. Compared with paper-order entry, prescribing HIT of varying sophistication was associated with decreased rates of prescribing errors (median OR 0.24, IQR 0.03-0.57). Ten BCTs were present in at least two successful interventions and may be effective components of prescribing HIT implementation and optimisation including prescriber involvement in system design, clinical colleagues as trainers, modification of HIT in response to feedback, direct observation of prescriber workflow, monitoring of electronic orders to detect errors, and system alerts that prompt the prescriber.

CONCLUSIONS

Prescribing HIT is associated with a reduction in prescribing errors in a variety of hospital settings. Poor reporting of intervention delivery and content limited the BCT analysis. More detailed reporting may have identified additional effective intervention components. Effective BCTs may be considered in the design and development of prescribing HIT and in the reporting and evaluation of future studies in this area.

Precision dosing to avoid adverse drug reactions

Adverse drug reactions (ADRs) have traditionally been managed by trial and error, adjusting drug and dose selection reactively following patient harm. With an improved understanding of ADRs, and the patient characteristics that increase susceptibility, precision medicine technologies enable a proactive approach to ADRs and support clinicians to change prescribing accordingly. This commentary revisits the famous pharmacology–toxicology continuum first postulated by Paracelsus 500 years ago and explains why precision dosing is needed to help avoid ADRs in modern clinical practice. Strategies on how to improve precision dosing are given, including more research to establish better precision dosing targets in the cases of greatest need, easier access to dosing instructions via e-prescribing, improved monitoring of patients with novel biomarkers of drug response, and further application of model-informed precision dosing.