The Economics of Medication Safety: A Cost-Benefits Analysis Framework for Evaluating an Electronic Medication System.

Medication prescribing in paediatrics is complex and compounded by the need to provide age and weight related doses, and errors continue to be problematic. Electronic medication systems (EMS) can reduce errors through dosing calculators and computerised decision support. However, evidence on costs and benefits of these systems is limited, particularly in paediatric hospitals. This paper presents the development of a cost-benefit analysis (CBA) framework to assess the impact of an EMS implementation in a paediatric tertiary hospital. An innovative component of the framework is the incorporation of the impact of the effects of the EMS for both the health system as well as for patients and their wider family networks, allowing a net social benefit assessment. We describe the impact of non-clinical out-of-pocket costs of admission and use discrete choice experiments to measure both medication related harm and the importance of medication safety to families and members of the community.

Measuring 21 low-value hospital procedures: claims analysis of Australian private health insurance data (2010-2014).

OBJECTIVE: To examine the prevalence, costs and trends (2010-2014) for 21 low-value inpatient procedures in a privately insured Australian patient cohort. DESIGN: We developed indicators for 21 low-value procedures from evidence-based lists such as Choosing Wisely, and applied them to a claims data set of hospital admissions. We used narrow and broad indicators where multiple low-value procedure definitions exist. SETTING AND PARTICIPANTS: A cohort of 376 354 patients who claimed for an inpatient service from any of 13 insurance funds in calendar years 2010-2014; approximately 7% of the privately insured Australian population. MAIN OUTCOME MEASURES: Counts and proportions of low-value procedures in 2014, and relative change between 2010 and 2014. We also report both the Medicare (Australian government) and the private insurance financial contributions to these low-value admissions. RESULTS: Of the 14 662 patients with admissions for at least 1 of the 21 procedures in 2014, 20.8%-32.0% were low-value using the narrow and broad indicators, respectively. Of the 21 procedures, admissions for knee arthroscopy were highest in both the volume and the proportion that were low-value (1607-2956; 44.4%-81.7%).Seven low-value procedures decreased in use between 2010 and 2014, while admissions for low-value percutaneous coronary interventions and inpatient intravitreal injections increased (51% and 8%, respectively).For this sample, we estimated 2014 Medicare contributions for admissions with low-value procedures to be between $A1.8 and $A2.9 million, and total charges between $A12.4 and $A22.7 million. CONCLUSIONS: The Australian federal government is currently reviewing low-value healthcare covered by Medicare and private health insurers. Estimates from this study can provide crucial baseline data and inform design and assessment of policy strategies within the Australian private healthcare sector aimed at curtailing the high volume and/or proportions of low-value procedures.

Measuring Hospital-Acquired Complications Associated With Low-Value Care.

Importance: Studies of low-value care have focused on the prevalence of low-value care interventions but have rarely quantified downstream consequences of these interventions for patients or the health care system. Objective: To measure immediate in-hospital harm associated with 7 low-value procedures. Design, Setting, and Participants: A cohort study with a descriptive analysis using hospital admission data from 225 public hospitals in New South Wales, Australia, was conducted from July 1, 2014, to June 30, 2017. All 9330 episodes involving 1 of 7 low-value procedures were evaluated, including endoscopy for dyspepsia in people younger than 55 years (3689 episodes); knee arthroscopy for osteoarthritis or meniscal tears (3963 episodes); colonoscopy for constipation in people younger than 50 years (665 episodes); endovascular repair of abdominal aortic aneurysm in asymptomatic, high-risk patients (508 episodes); carotid endarterectomy in asymptomatic, high-risk patients (273 episodes); renal artery angioplasty (176 episodes); and spinal fusion for uncomplicated low back pain (56 episodes). Sixteen hospital-acquired complications (HACs) were used as a measure of harm associated with low-value care. Main Outcomes and Measures: For each low-value procedure, the percentage associated with any HAC and the difference in mean length of stay for patients receiving low-value care with and without HACs were calculated. Results: Across the 225 hospitals and 9330 episodes of low-value care, rates of HACs were low for low-value endoscopy (4 [0.1%] episodes; 95% CI, 0.02%-0.2%), knee arthroscopy (18 [0.5%] episodes; 95% CI, 0.2%-0.7%), and colonoscopy (2 [0.3%] episodes; 95% CI, 0.0%-0.9%) but higher for low-value spinal fusion (4 [7.1%] episodes; 95% CI, 2.2%-11.5%), endovascular repair of abdominal aortic aneurysm (76 [15.0%] episodes; 95% CI, 11.1%-19.7%), carotid endarterectomy (21 [7.7%] episodes; 95% CI, 5.2%-10.1%), and renal artery angioplasty (15 [8.5%] episodes; 95% CI, 5.8%-11.5%). For most procedures, the most common HAC was health care-associated infection, which accounted for 83 (26.3%) (95% CI, 21.8%-31.5%) of all HACs observed. The highest rate of health care-associated infection was 8.4% (95% CI, 5.2%-11.4%) for renal artery angioplasty. For all 7 low-value procedures, median length of stay for patients with an HAC was 2 times or more the median length of stay for patients without a complication. For example, median length of stay was 1 (interquartile range [IQR], 1-1) day for knee arthroscopy with no HACs but increased to 10.5 (IQR, 1.0-21.3) days for patients with an HAC. Conclusions and Relevance: These findings suggest that use of these 7 procedures in patients who probably should not receive them is harming some of those patients, consuming additional hospital resources, and potentially delaying care for other patients for whom the services would be appropriate. Although only some immediate consequences of just 7 low-value services were examined, harm related to all low-value procedures was noted, including high rates of harm for certain higher-risk procedures. The full burden of low-value care for patients and the health system is yet to be quantified.

A methodological protocol for selecting and quantifying low-value prescribing practices in routinely collected data: an Australian case study.

BACKGROUND: Growing imperatives for safety, quality and responsible resource allocation have prompted renewed efforts to identify and quantify harmful or wasteful (low-value) medical practices such as test ordering, procedures and prescribing. Quantifying these practices at a population level using routinely collected health data allows us to understand the scale of low-value medical practices, measure practice change following specific interventions and prioritise policy decisions. To date, almost all research examining health care through the low-value lens has focused on medical services (tests and procedures) rather than on prescribing. The protocol described herein outlines a program of research funded by Australia's National Health and Medical Research Council to select and quantify low-value prescribing practices within Australian routinely collected health data. METHODS: We start by describing our process for identifying and cataloguing international low-value prescribing practices. We then outline our approach to translate these prescribing practices into indicators that can be applied to Australian routinely collected health data. Next, we detail methods of using Australian health data to quantify these prescribing practices (e.g. prevalence of low-value prescribing and related costs) and their downstream health consequences. We have approval from the necessary Australian state and commonwealth human research ethics and data access committees to undertake this work. DISCUSSION: The lack of systematic and transparent approaches to quantification of low-value practices in routinely collected data has been noted in recent reviews. Here, we present a methodology applied in the Australian context with the aim of demonstrating principles that can be applied across jurisdictions in order to harmonise international efforts to measure low-value prescribing. The outcomes of this research will be submitted to international peer-reviewed journals. Results will also be presented at national and international pharmacoepidemiology and health policy forums such that other jurisdictions have guidance to adapt this methodology.

Assessing measures of comorbidity and functional status for risk adjustment to compare hospital performance for colorectal cancer surgery: a retrospective data-linkage study.

BACKGROUND: Comparing outcomes between hospitals requires consideration of patient factors that could account for any observed differences. Adjusting for comorbid conditions is common when studying outcomes following cancer surgery, and a commonly used measure is the Charlson comorbidity index. Other measures of patient health include the ECOG performance status and the ASA physical status score. This study aimed to ascertain how frequently ECOG and ASA scores are recorded in population-based administrative data collections in New South Wales, Australia and to assess the contribution each makes in addition to the Charlson comorbidity index in risk adjustment models for comparative assessment of colorectal cancer surgery outcomes between hospitals. METHODS: We used linked administrative data to identify 6964 patients receiving surgery for colorectal cancer in 2007 and 2008. We summarised the frequency of missing data for Charlson comorbidity index, ECOG and ASA scores, and compared patient characteristics between those with and without these measures. The performance of ASA and ECOG in risk adjustment models that also included Charlson index was assessed for three binary outcomes: 12-month mortality, extended length of stay and 28-day readmission. Patient outcomes were compared between hospital peer groups using multilevel logistic regression analysis. RESULTS: The Charlson comorbidity index could be derived for all patients, ASA score was recorded for 78 % of patients and ECOG performance status recorded for only 24 % of eligible patients. Including ASA or ECOG improved the predictive ability of models, but there was no consistently best combination. The addition of ASA or ECOG did not substantially change parameter estimates for hospital peer group after adjusting for Charlson comorbidity index. CONCLUSIONS: While predictive ability of regression models is maximised by inclusion of one or both of ASA score and ECOG performance status, there is little to be gained by adding ASA or ECOG to models containing the Charlson comorbidity index to address confounding. The Charlson comorbidity index has good performance and is an appropriate measure to use in risk adjustment to compare outcomes between hospitals.