Measuring the impact of pharmaceutical care bundle delivery on patient outcomes: an observational study.

BACKGROUND: Clinical pharmacists perform activities to optimise medicines use and prevent patient harm. Historically, clinical pharmacy quality indicators have measured individual activities not linked to patient outcomes. AIM: To determine the proportion of patients who receive a pharmaceutical care bundle (PCB) (consisting of a medication history, medication review, discharge medication list and medicines information on the discharge summary) as well as investigate the relationship between delivery of this PCB and patient outcomes. METHOD: Pharmaceutical care bundle activities were defined within state-wide (Queensland, Australia) clinical information systems and datasets were linked. An observational study using routinely recorded data was performed at ten participating sites for adult patients who had a non-same day hospital stay. The association between extent of PCB delivery and three patient outcomes were investigated: length of stay (LOS), unplanned readmission, and mortality. RESULTS: In total 283,813 patient hospital stays were evaluated. The delivery of the PCB occurred in 26.9% of patients at the ten participating hospital sites, ranging from 0.6 to 61.2% across sites. Patients with a longer LOS were more likely to receive delivery of the complete PCB (P < 0.001). There was no correlation between PCB and hospital standardised mortality ratio (r = 0.03, p = 0.93). Higher rates of delivery of the PCB were associated with lower rates of unplanned readmission within 30 days (r = - 0.993, p < 0.001). CONCLUSION: A complete PCB was delivered to 26.9% of patients and was associated with a significantly lower rate of unplanned readmission within 30 days.

Defining quality indicators, pharmaceutical care bundles and outcomes of clinical pharmacy service delivery using a Delphi consensus approach.

BACKGROUND: Clinical pharmacy quality indicators are often non-uniform and measure individual activities not linked to outcomes. AIM: To define a consensus agreed pharmaceutical care bundle and patient outcome measures across an entire state health service. METHOD: A four-round modified-Delphi approach with state Directors of Pharmacy was performed (n = 25). They were asked to rate on a 5-point Likert scale the relevance and measurability of 32 inpatient clinical pharmacy quality indicators and outcome measures. They also ranked clinical pharmacy activities in order from perceived most to least beneficial. Based upon these results, pharmaceutical care bundles consisting of multiple clinical pharmacy activities were formed, and relevance and measurability assessed. RESULTS: Response rate ranged from 40 to 60%. Twenty-six individual clinical pharmacy quality indicators reached consensus. The top ranked clinical pharmacy quality indicator was 'proportion of patients where a pharmacist documents an accurate list of medicines during admission'. There were nine pharmaceutical care bundles formed consisting between 3 and 7 activities. Only one pharmaceutical care bundle reached consensus: medication history, adverse drug reaction/allergy documentation, admission and discharge medication reconciliation, medication review, provision of medicines education and provision of a medication list on discharge. Sixteen outcome measures reached consensus. The top ranked were hospital acquired complications, readmission due to medication misadventure and unplanned readmission within 10 days. CONCLUSION: Consensus has been reached on one pharmaceutical care bundle and sixteen outcomes to monitor clinical pharmacy service delivery. The next step is to measure the extent of pharmaceutical care bundle delivery and the link to patient outcomes.

The impact of transition to a digital hospital on medication errors (TIME study).

Digital transformation in healthcare improves the safety of health systems. Within our health service, a new digital hospital has been established and two wards from a neighbouring paper-based hospital transitioned into the new digital hospital. This created an opportunity to evaluate the impact of complete digital transformation on medication safety. Here we discuss the impact of transition from a paper-based to digital hospital on voluntarily reported medication incidents and prescribing errors. This study utilises an interrupted time-series design and takes place across two wards as they transition from a paper to a digital hospital. Two data sources are used to assess impacts on medication incidents and prescribing errors: (1) voluntarily reported medication incidents and 2) a chart audit of medications prescribed on the study wards. The chart audit collects data on procedural, dosing and therapeutic prescribing errors. There are 588 errors extracted from incident reporting software during the study period. The average monthly number of errors reduces from 12.5 pre- to 7.5 post-transition (p < 0.001). In the chart audit, 5072 medication orders are reviewed pre-transition and 3699 reviewed post-transition. The rates of orders with one or more error reduces significantly after transition (52.8% pre- vs. 15.7% post-, p < 0.001). There are significant reductions in procedural (32.1% pre- vs. 1.3% post-, p < 0.001), and dosing errors (32.3% pre- vs. 14% post-, p < 0.001), but not therapeutic errors (0.6% pre- vs. 0.7% post-, p = 0.478). Transition to a digital hospital is associated with reductions in voluntarily reported medication incidents and prescribing errors.

Evaluation of the nature, severity, likelihood and preventability of medication-related hospital-acquired complications.

Objective Pricing for safety and quality was introduced into Australian hospitals using a defined list of hospital-acquired complications (HACs). Medication-related HACs include drug-related respiratory complications (DRRC), haemorrhagic disorder due to circulating anticoagulants (HDDCA) and hypoglycaemia. The aim of this study was to determine the probability, severity and preventability of medication-related HACs, common contributory medications and themes, and whether medication-related HACs are a suitable data source to inform risk associated with medicines use. Methods Medical notes were reviewed retrospectively for all patients discharged from a tertiary referral metropolitan hospital between 1 July and 31 December 2018 who were flagged as experiencing a medication-related HAC. Naranjo, Hartwig's and Schumock and Thornton tools were used to assess the probability, severity and preventability of medication-related HACs. Results Over the 6-month period, 88 patients experienced a medication-related HAC. An HAC was not identified in five (5.7%) patient charts. The most common HAC was hypoglycaemia (n=59; 67%), followed by HDDCA (n=23; 26%) and DRRC (n=6; 7%). Fifteen patients (17%) flagged with a hypoglycaemia HAC were not on a medicine associated with hypoglycaemia. Overall, 6% (n=4) of HACs were severe, 72% (n=49) were moderate and 22% (n=15) were mild. Where the HAC and causal medication(s) were identified (n=68), over half were probable (51.5%, n=35) and 44.1% (n=30) were possible causes of the adverse drug reaction; only two (2.9%) were definite causes. None of the DRRC HACs was preventable. Over half the HDDCA HACs (52.2%; n=12) and almost half the hypoglycaemia HACs (46.2%; n=18) were not preventable. Common themes included appropriate anticoagulant agent, dose and monitoring, as well as periprocedural hypoglycaemic management, which considers oral intake and comorbidities. Conclusion Not all patients who experience medication-related HACs were on causative medications. Of those who were, medications were probable causal agents in over 50% of cases. Only a small number of HACs were severe and under half of medication-related HACs were preventable. What is known about the topic? The relationship between pricing for safety and quality and improvements in patient outcomes has shown mixed results. Medication-related harm is a problem within Australia and system-wide changes should be considered to improve patient care. What does this paper add? This paper adds evidence to the use of medication-related HACs as a source of data to inform risk associated with medicines use and provides details on the preventability and severity of medication-related HACs and the likelihood that medicines contribute to these complications. What are the implications for practitioners? This paper provides clinicians and policy makers details on the utility of using medication-related HACs as a measure of risk associated with medicines use. It discusses merit in using HACs as a source for quality improvement, but recommends that definitions may need to be reviewed to enhance utility.

A stain on iron therapy.

Iron staining is an unwanted and in some cases permanent adverse effect of intravenous iron administration. Cosmetically unacceptable staining may cause distress and have psychological implications for the patient There should be a suitable indication for parenteral iron therapy. Patients must be advised of the risk of harm and give their informed consent before receiving parenteral iron Strategies to minimise the risks of staining with intravenous iron include appropriate cannulation and close monitoring of the infusion. Stop the infusion if there are signs of extravasation Laser therapy may be a treatment option in cases of persistent discolouration due to iron staining

Accuracy of best possible medication history documentation by pharmacists at an Australian tertiary referral metropolitan hospital.

AIM: To determine the quality of best possible medication history (BPMH) taking activities undertaken by pharmacists. To identify factors which impact upon erroneous documentation. To assess risks associated with erroneous documentation of BPMH by pharmacists. METHOD: A clinical pharmacist randomly selected patients across a tertiary referral, metropolitan hospital over an 9-day period and documented comparator medication histories (CMHs) using a structured interview. BPMH documented by pharmacists as part of routine care and CMH were compared, and erroneous documentation was classified according to previous definitions in the literature. Erroneous documentation was risk stratified. RESULTS: 99 BPMH and CMH were compared. There were 14 medication omissions which occurred across 10 patients and 14 discrepancies across 12 patients. There was no association identified between erroneous documentation and pharmacist seniority/experience (p=0.25), where BPMH taken (p=0.7), day of week BPMH documented (p=0.45) or time since admission to when BPMH was documented (p=1). Patient age did not impact erroneous documentation rates (p=0.22). There was an association between the number of sources used to confirm a medication history and erroneous documentation incidence (p=0.035). The number of medications increased the rate of documentation error. While 85.19% (n=115) of erroneous documentation were deemed unlikely to cause patient discomfort or clinical deterioration, 1.48% (n=2) had the potential to result in severe discomfort or clinical deterioration. CONCLUSION: Six out of seven BPMH documented by pharmacists as part of usual clinical practice are accurate. Major influences on accuracy include the number of medications and sources used. There is a low possibility that erroneous documentation by pharmacists will cause harm.