Mitigating or reducing the risk of medication harm is a global policy priority. But evidence reflecting preventable medication harm in medical care and the factors that derive this harm remain unknown. Therefore, we aimed to quantify the prevalence, severity and type of preventable medication harm across medical care settings. We performed a systematic review and meta-analysis of observational studies to compare the prevalence of preventable medication harm. Searches were carried out in Medline, Cochrane library, CINAHL, Embase and PsycINFO from 2000 to 27 January 2020. Data extraction and critical appraisal was undertaken by two independent reviewers. Random-effects meta-analysis was employed followed by univariable and multivariable meta-regression. Heterogeneity was quantified using the I2 statistic, and publication bias was evaluated. PROSPERO: CRD42020164156. Of the 7780 articles, 81 studies involving 285,687 patients were included. The pooled prevalence for preventable medication harm was 3% (95% confidence interval (CI) 2 to 4%, I2 = 99%) and for overall medication harm was 9% (95% CI 7 to 11%, I2 = 99.5%) of all patient incidence records. The highest rates of preventable medication harm were seen in elderly patient care settings (11%, 95% 7 to 15%, n = 7), intensive care (7%, 4 to 12%, n = 6), highly specialised or surgical care (6%, 3 to 11%, n = 13) and emergency medicine (5%, 2 to 12%, n = 12). The proportion of mild preventable medication harm was 39% (28 to 51%, n = 20, I2 = 96.4%), moderate preventable harm 40% (31 to 49%, n = 22, I2 = 93.6%) and clinically severe or life-threatening preventable harm 26% (15 to 37%, n = 28, I2 = 97%). The source of the highest prevalence rates of preventable harm were at the prescribing (58%, 42 to 73%, n = 9, I2 = 94%) and monitoring (47%, 21 to 73%, n = 8, I2 = 99%) stages of medication use. Preventable harm was greatest in medicines affecting the ‘central nervous system’ and ‘cardiovascular system’. This is the largest meta-analysis to assess preventable medication harm. We conclude that around one in 30 patients are exposed to preventable medication harm in medical care, and more than a quarter of this harm is considered severe or life-threatening. Our results support the World Health Organisation’s push for the detection and mitigation of medication-related harm as being a top priority, whilst highlighting other key potential targets for remedial intervention that should be a priority focus for future research.

/pdf/preventable-medication-harm-across-health-care-settings-a-1axu36ppa4.pdf

Preventable medication harm across health care settings: a systematic review and meta-analysis

Objective The aim of this research was to ascertain the effectiveness of current electronic prescribing (EP) systems to prevent a standardised set of paediatric prescribing errors likely to cause harm if they reach the patient. Design Semistructured survey. Setting UK hospitals using EP in the paediatric setting. Outcome measures Number and type of erroneous orders able to be prescribed, and the level of clinical decision support (CDS) provided during the prescribing process. Results 90.7% of the erroneous orders were able to be prescribed across the seven different EP systems tested. Levels of CDS varied between systems and between sites using the same system. Conclusions EP systems vary in their ability to prevent harmful prescribing errors in the hospital paediatric setting. Differences also occur between sites using the same system, highlighting the importance of how a system is set up and optimised.

https://adc.bmj.com/content/archdischild/early/2019/06/07/archdischild-2019-316859.full.pdf

Electronic prescribing in paediatric secondary care: are harmful errors prevented?

Aims
The aim of the study was to develop a list of hospital based paediatric prescribing indicators that can be used to assess the impact of electronic prescribing or clinical decision support tools on paediatric prescribing errors.

Methods
Two rounds of an electronic consensus method (eDelphi) were carried out with 21 expert panellists from the UK. Panellists were asked to score each prescribing indicator for its likelihood of occurrence and severity of outcome should the error occur. The scores were combined to produce a risk score and a median score for each indicator calculated. The degree of consensus between panellists was defined as the proportion that gave a risk score in the same category as the median. Indicators were included if a consensus of 80% or higher was achieved and were in the high risk categories.

Results
Each of the 21 panellists completed an exploratory round and two rounds of scoring. This identified 41 paediatric prescribing indicators with a high risk rating and greater than 80% consensus. The most common error type within the indicators was wrong dose (n = 19) and the most common drug classes were antimicrobials (n = 10) and cardiovascular (n = 7).

Conclusions
A set of 41 paediatric prescribing indicators describing potential harm for the hospital setting has been identified by an expert panel. The indicators provide a standardized method of evaluation of prescribing data on both paper and electronic systems. They can also be used to assess implementation of clinical decision support systems or other quality improvement initiatives.

/pdf/developing-consensus-on-hospital-prescribing-indicators-of-5gqpcivfjs.pdf

Developing consensus on hospital prescribing indicators of potential harm for infants and children.

There is now widespread awareness of the very considerable burden of harm and associated costs resulting from medication errors, which, in turn, has stimulated national and international drives to reduce medication-associated harm. In parallel, there is a growing appreciation that health information technology (HIT) has the potential to reduce the risk of medication errors. There is, however, a wide gulf between HIT as a structural intervention and its translation into improvements in care processes , and a wider gulf still between the process of care and improvements in health outcomes .1 What matters to patients, and their loved ones, is of course avoidance of actual harm and it is for this reason that the WHO, in launching its Third Global Safety Challenge, called it ‘Medication Without Harm’.2 

Governments across the world are investing substantial sums of money in moving care from paper-based records to electronic health record (EHR) infrastructures. A key driver for this move is the belief that this will result in substantial improvements in patient safety.3 A high frequency of medication errors and preventable adverse drug events have been documented in many studies of patient safety problems, making medication safety an obvious place to start. Yet, the analysis by Holmgren et al reported in this edition demonstrates that current EHRs would fail to prevent over one-third of potentially serious medication errors in a sample of 1527 hospitals in the USA.4 …

https://qualitysafety.bmj.com/content/qhc/29/1/7.full.pdf

Realising the potential of health information technology to enhance medication safety

OBJECTIVE Quality indicators (QIs) are an important mechanism by which health services can be evaluated. We aimed to develop a set of QIs for pharmacist home visit services and assess their measurement properties. METHODS A three-step procedure was applied: (1) Selection of existing content-validated QIs from the international literature and the development of QIs based on national guidelines and home healthcare professionals' opinions; (2) Expert panel consensus of a preliminary set of QIs using the RAND/UCLA Appropriateness Method; (3) Field testing to evaluate their measurement properties (feasibility, applicability, improvement potential, discriminatory capacity, sensitivity to change, acceptability and implementation issues) followed by exploratory semistructured interviews in Japan. RESULTS Fifty-two preliminary QIs were prepared and 45 were judged as "appropriate"by the expert panel. Sixty-one community pharmacies were recruited to this study with 41 contributing QI data monthly over the 6-month period. Field testing showed that 20 QIs met six measurement properties (ie, feasibility, applicability, improvement potential, discriminatory capacity, acceptability and implementation issues). Nine of these QIs also had high sensitivity to change. Additionally, interviews identified that the main positive impact on practice of using QIs was the early detection of causes of drug-related problems but a negative impact was decrease of pharmacists' motivation. Auto extraction of QIs was seen as a major facilitator, given the time taken to manually extract QI data. CONCLUSIONS A set of QIs for pharmacist home visit services was rigorously developed and tested. This QI set may be useful in evaluating the quality of such services.

Developing and testing a set of quality indicators for pharmacist home visit services: A mixed methods study in Japan.

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Developing consensus on hospital prescribing indicators of potential harm for infants and children.