Estimated impact from the withdrawal of primary care financial incentives on selected indicators of quality of care in Scotland: controlled interrupted time series analysis.

OBJECTIVE: To determine whether the withdrawal of the Quality and Outcomes Framework (QOF) scheme in primary care in Scotland in 2016 had an impact on selected recorded quality of care, compared with England where the scheme continued. DESIGN: Controlled interrupted time series regression analysis. SETTING: General practices in Scotland and England. PARTICIPANTS: 979 practices with 5 599 171 registered patients in Scotland, and 7921 practices with 56 270 628 registered patients in England in 2013-14, decreasing to 864 practices in Scotland and 6873 in England in 2018-19, mainly due to practice mergers. MAIN OUTCOME MEASURES: Changes in quality of care at one year and three years after withdrawal of QOF financial incentives in Scotland at the end of the 2015-16 financial year for 16 indicators (two complex processes, nine intermediate outcomes, and five treatments) measured annually for financial years from 2013-14 to 2018-19. RESULTS: A significant decrease in performance was observed for 12 of the 16 quality of care indicators in Scotland one year after QOF was abolished and for 10 of the 16 indicators three years after QOF was abolished, compared with England. At three years, the absolute percentage point difference between Scotland and England was largest for recording (by tick box) of mental health care planning (-40.2 percentage points, 95% confidence interval -45.5 to -35.0) and diabetic foot screening (-22.8, -33.9 to -11.7). Substantial reductions were, however, also observed for intermediate outcomes, including blood pressure control in patients with peripheral arterial disease (-18.5, -22.1 to -14.9), stroke or transient ischaemic attack (-16.6, -20.6 to -12.7), hypertension (-13.7, -19.4 to -7.9), diabetes (-12.7, -15.0 to -12.4), or coronary heart disease (-12.8, -14.9 to -10.8), and for glycated haemoglobin control in people with HbA1c levels ≤75 mmol/mol (-5.0, -8.4 to -1.5). No significant differences were observed between Scotland and England for influenza immunisation and antiplatelet or anticoagulant treatment for coronary heart disease three years after withdrawal of incentives. CONCLUSION: The abolition of financial incentives in Scotland was associated with reductions in recorded quality of care for most performance indicators. Changes to pay for performance should be carefully designed and implemented to monitor and respond to any reductions in care quality.

Quality of acute myocardial infarction care in England and Wales during the COVID-19 pandemic: linked nationwide cohort study.

BACKGROUND AND OBJECTIVE: The impact of the COVID-19 pandemic on the quality of care for patients with acute myocardial infarction (AMI) is uncertain. We aimed to compare quality of AMI care in England and Wales during and before the COVID-19 pandemic using the 2020 European Society of Cardiology Association for Acute Cardiovascular Care quality indicators (QIs) for AMI. METHODS: Cohort study of linked data from the AMI and the percutaneous coronary intervention registries in England and Wales between 1 January 2017 and 27 May 2020 (representing 236 743 patients from 186 hospitals). At the patient level, the likelihood of attainment for each QI compared with pre COVID-19 was calculated using logistic regression. The date of the first national lockdown in England and Wales (23 March 2020) was chosen for time series comparisons. RESULTS: There were 10 749 admissions with AMI after 23 March 2020. Compared with before the lockdown, patients admitted with AMI during the first wave had similar age (mean 68.0 vs 69.0 years), with no major differences in baseline characteristics (history of diabetes (25% vs 26%), renal failure (6.4% vs 6.9%), heart failure (5.8% vs 6.4%) and previous myocardial infarction (22.9% vs 23.7%)), and less frequently had high Global Registry of Acute Coronary Events risk scores (43.6% vs 48.6%). There was an improvement in attainment for 10 (62.5%) of the 16 measured QIs including a composite QI (43.8% to 45.2%, OR 1.06, 95% CI 1.02 to 1.10) during, compared with before, the lockdown. CONCLUSION: During the first wave of the COVID-19 pandemic in England and Wales, quality of care for AMI as measured against international standards did not worsen, but improved modestly.

Core outcome sets through the healthcare ecosystem: the case of type 2 diabetes mellitus.

BACKGROUND: It is increasingly accepted that insufficient attention has been given to the patient health outcomes that are important to measure in comparative effectiveness research that will inform decision-making. The relationship between outcomes chosen for comparative effectiveness research, outcomes used in decision-making in routine care, and outcome data recorded in electronic health records (EHR) is also poorly understood. The COMET Initiative (http://www.comet-initiative.org/. Accessed 3 Apr 2020) supports and encourages the development and use of 'core outcome sets' (COS), which represent the minimum set of patient health outcomes that should be measured and reported for a specific condition. There is growing interest in identifying how COS might fit into the different stages of the healthcare research and delivery ecosystem, and whether inclusion in the EHR might facilitate this. METHODS: We sought to determine the degree of overlap between outcomes within COS for research and routine care, EMA, FDA and NICE guidelines, NICE quality statements/indicators, EHR and a point-of-care randomised clinical trial, using type 2 diabetes (T2D) as a case study. RESULTS: There is substantial agreement about important patient outcomes for T2D for research and healthcare, with associated coverage within the UK general practice EHR. CONCLUSIONS: This case study has demonstrated the potential for efficient research and value-based healthcare when the EHR can include COS for both research and care, where the COS comprises outcomes of importance to all relevant stakeholders. However, this concordance may not hold more generally, as the focus on patient-centred outcomes may well be greater in T2D than in other conditions. Work is ongoing to examine other clinical areas, in order to highlight any current inefficiencies when health outcomes in research and healthcare do not agree with core outcomes identified by patients, clinicians and other key stakeholders.

Quality of Care in the United Kingdom after Removal of Financial Incentives.

BACKGROUND: The benefits of pay-for-performance schemes in improving the quality of care remain uncertain. There is little information on the effect of removing incentives from existing pay-for-performance schemes. METHODS: We conducted interrupted time-series analyses of electronic medical record (EMR) data from 2010 to 2017 for 12 quality-of-care indicators in the United Kingdom's Quality and Outcomes Framework for which financial incentives were removed in 2014 and 6 indicators for which incentives were maintained. We estimated the effects of removing incentives on changes in performance on quality-of-care measures. RESULTS: Complete longitudinal data were available for 2819 English primary care practices with more than 20 million registered patients. There were immediate reductions in documented quality of care for all 12 indicators in the first year after the removal of financial incentives. Reductions were greatest for indicators related to health advice, with a reduction of 62.3 percentage points (95% confidence interval [CI], -65.6 to -59.0) in EMR documentation of lifestyle counseling for patients with hypertension. Changes were smaller for indicators involving clinical actions that automatically update the EMR, such as laboratory testing, with a reduction of 10.7 percentage points (95% CI, -13.6 to -7.8) in control of cholesterol in patients with coronary heart disease and 12.1 percentage points (95% CI, -13.6 to -10.6) for thyroid-function testing in patients with hypothyroidism. There was little change in performance on the 6 quality measures for which incentives were maintained. CONCLUSIONS: Removal of financial incentives was associated with an immediate decline in performance on quality measures. In part, the decline probably reflected changes in EMR documentation, but declines on measures involving laboratory testing suggest that incentive removal also changed the care delivered.

The NICE process for developing quality standards and indicators.

This paper describes the core principles and processes used by the National Institute for Health and Care Excellence (NICE) to develop quality standards and quality indicators from evidence based guidelines.

Budget impact analysis-principles of good practice: report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force.

BACKGROUND: Budget impact analyses (BIAs) are an essential part of a comprehensive economic assessment of a health care intervention and are increasingly required by reimbursement authorities as part of a listing or reimbursement submission. OBJECTIVES: The objective of this report was to present updated guidance on methods for those undertaking such analyses or for those reviewing the results of such analyses. This update was needed, in part, because of developments in BIA methods as well as a growing interest, particularly in emerging markets, in matters related to affordability and population health impacts of health care interventions. METHODS: The Task Force was approved by the International Society for Pharmacoeconomics and Outcomes Research Health Sciences Policy Council and appointed by its Board of Directors. Members were experienced developers or users of BIAs; worked in academia and industry and as advisors to governments; and came from several countries in North America and South America, Oceania, Asia, and Europe. The Task Force solicited comments on the drafts from a core group of external reviewers and, more broadly, from the membership of the International Society for Pharmacoeconomics and Outcomes Research. RESULTS: The Task Force recommends that the design of a BIA for a new health care intervention should take into account relevant features of the health care system, possible access restrictions, the anticipated uptake of the new intervention, and the use and effects of the current and new interventions. The key elements of a BIA include estimating the size of the eligible population, the current mix of treatments and the expected mix after the introduction of the new intervention, the cost of the treatment mixes, and any changes expected in condition-related costs. Where possible, the BIA calculations should be performed by using a simple cost calculator approach because of its ease of use for budget holders. In instances, however, in which the changes in eligible population size, disease severity mix, or treatment patterns cannot be credibly captured by using the cost calculator approach, a cohort or patient-level condition-specific model may be used to estimate the budget impact of the new intervention, accounting appropriately for those entering and leaving the eligible population over time. In either case, the BIA should use data that reflect values specific to a particular decision maker's population. Sensitivity analysis should be of alternative scenarios chosen from the perspective of the decision maker. The validation of the model should include at least face validity with decision makers and verification of the calculations. Data sources for the BIA should include published clinical trial estimates and comparator studies for the efficacy and safety of the current and new interventions as well as the decision maker's own population for the other parameter estimates, where possible. Other data sources include the use of published data, well-recognized local or national statistical information, and, in special circumstances, expert opinion. Reporting of the BIA should provide detailed information about the input parameter values and calculations at a level of detail that would allow another modeler to replicate the analysis. The outcomes of the BIA should be presented in the format of interest to health care decision makers. In a computer program, options should be provided for different categories of costs to be included or excluded from the analysis. CONCLUSIONS: We recommend a framework for the BIA, provide guidance on the acquisition and use of data, and offer a common reporting format that will promote standardization and transparency. Adherence to these good research practice principles would not necessarily supersede jurisdiction-specific BIA guidelines but may support and enhance local recommendations or serve as a starting point for payers wishing to promulgate methodology guidelines.