Introducing activity-based financing: a review of experience in Australia, Denmark, Norway and Sweden

We review and evaluate the international literature on activity-based funding of health services, focussing especially on experience in Australia (Victoria), Denmark, Norway and Sweden. In evaluating this literature we summarise the differences and pros and cons of three different funding arrangements, namely cost-based reimbursement, global budgeting and activity-based financing. The institutional structures of the four jurisdictions that are the main focus of the review are described, and an outline is provided about how activity-based funding has been introduced in each. We then turn to the mechanics of activity-based funding and discuss in detail how patients are classified, how prices are set and how other services are funded. Although concentrating on the four jurisdictions, we draw on wider international experience to inform this discussion. We review evidence of the impact of activity-based funding in the four jurisdictions on efficiency, activity rates, waiting times, quality and overall expenditure. Finally we conclude with a brief commentary of some of the challenges that would have to be faced if implementing activity-based funding.

Establishing a Fair Playing Field for Payment by Results

The English government has encouraged private providers – known as Independent Sector Treatment Centres (ISTCs) – to treat publicly funded (NHS) patients. Providers are paid a fixed price per patient treated, adjusted to reflect geographical differences in input costs. But there may be other legitimate cost variations between providers. This report considers the regulatory and production-process constraints that could cause public and private providers costs to differ. Most of these exogenous cost differentials can be rectified by adjustments to the regulatory system or to the payment method. We find evidence that ISTCs are treating different types of patients than NHS hospitals. If these differences drive costs, payments for treatment might need to be differentiated by setting.

How well do DRGs for appendectomy explain variations in resource use? : An analysis of patient-level data from 10 European countries

Appendectomy is a common and relatively simple procedure to remove an inflamed appendix, but the rate of appendectomy varies widely across Europe. This paper investigates factors that explain differences in resource use for appendectomy. We analysed 106,929 appendectomy patients treated in 939 hospitals in ten European countries. In stage one, we tested the performance of three models in explaining variation in the (log of) cost of the inpatient stay (seven countries) or length-of-stay (three countries). The first model used only the Diagnosis Related Groups (DRGs) to which patients were coded; the second used a core set of general patient-level and appendectomy-specific variables; and the third model combined both sets of variables. In stage two, we investigated hospital-level variation. In classifying appendectomy patients, most DRG systems take account of complex diagnoses and comorbidities, but use different numbers of DRGs (range: 2 to 8). The capacity of DRGs and patient-level variables to explain patient-level cost variation ranges from 34% in Spain to over 60% in England and France. All DRG systems can make better use of administrative data such as the patient’s age, diagnoses and procedures, and all countries have outlying hospitals that could improve their management of resources for appendectomy

Why do patients having coronary artery bypass grafts have different costs or length of stay? : An analysis across ten European countries

We analyse variations in costs or lengths of stay (LoS) for 66,587 patients from ten European countries receiving a coronary artery bypass graft (CABG) procedure. In five of these countries, variations in cost are analysed using log-linear models. In the other five countries, negative binomial regression models are used to explore variations in LoS. We compare how well each country’s Diagnosis Related Group (DRG) system and a set of patient-level characteristics explain these variations. The most important explanatory factors are the total number of diagnoses and procedures, although no clear effects are evident for our CABG-specific diagnostic and procedural variables. Wound infections significantly increase length of stay and costs in all countries. There is no evidence that countries using larger numbers of DRGs to group CABG patients were better at explaining variations in cost or LoS. However, refinements to the construction of DRGs to group CABG patients might recognise first and subsequent CABGs or other specific surgical procedures, such as multiple valve repair

Group ramsey theory

AbstractA subset S of a group G is said to be a sum-free set if S ∩ (S + S) = ⊘. Such a set is maximal if for every sum-free set T ⊆ G, we have |T| ⩽ |S|. Here, we generalize this concept, defining a sum-free set S to be locally maximal if for every sum free set T such that S ⊆ T ⊆ G, we have S = T. Properties of locally maximal sum-free sets are studied and the sets are determined (up to isomorphism) for groups of small order

Unpacking the new proposed regulations for South African traditional health practitioners

South Africa (SA) has legislation that regulates almost all of its healthcare systems. The Traditional Health Practitoners Act finally provides legitimisation of an overwhelmingly popular indigenous healthcare system. However, as a consequence of the legal acknowledgement of traditional health practitioners, traditional medicine products must now also be brought under regulatory measures. If traditional medicines are to be prescribed, marketed and sold as part of a healthcare system recognised under SA law, they must meet the same stringent standards

Why are there long waits at English Emergency Departments?

A core performance target for the English National Health Service (NHS) concerns waiting times at Emergency Departments (EDs), with the aim of minimising long waits. We investigate the drivers of long waits. We analyse weekly data for all major EDs in England from April 2011 to March 2016. A Poisson model with ED fixed effects is used to explore the impact on long (> 4 hour) waits of variations in demand (population need and patient case-mix) and supply (emergency physicians, introduction of a Minor Injury Unit (MIU), inpatient bed occupancy, delayed discharges and long-term care). We assess overall ED waits and waits on a trolley (gurney) before admission. We also investigate variation in performance among EDs. The rate of long overall waits is higher in EDs serving older patients (4.2%), where a higher proportion of attendees leave without being treated (15.1%), in EDs with a higher death rate (3.3%) and in those located in hospitals with greater bed occupancy (1.5%). These factors are also significantly associated with higher rates of long trolley waits. The introduction of a co-located MIU is significantly and positively associated with long overall waits, but not with trolley waits.. There is substantial variation in waits among EDs that cannot be explained by observed demand and supply characteristics. The drivers of long waits are only partially understood but addressing them is likely to require a multi-faceted approach. EDs with high rates of unexplained long waits would repay further investigation to ascertain how they might improve

End-of-life care for aged care residents presenting to emergency departments

Background: The number of patients from Aged Care presenting to acute care is increasing, many of whom have a life limiting illness. Aims: To identify differences in relation to Aged Care Residents presenting to Emergency departments who died during a hospital admission compared to those who were referred to the hospital based palliative care. Methods: Review of a stratified random sample of 90 Aged Care residents transferred to acute care who died during admission in 2009; half the sample received palliative care. Comparisons were made with regard to age; gender; co-morbidities; symptoms, investigations and active treatment; prior admissions and costs. Results: The median age of patients was 87.5 years, 61% were female and 38% had three or more admissions in the year prior to death. Patients with a length of stay of four or more days were 2.98 times (CI, 95%:1.11-8.03) and patients with agitation were 3.08 (CI 95%:1.10- 8.64) times more likely to be referred to palliative care. Patients who received palliative care had significantly fewer investigations or active treatment in the 24 hours prior to their death (p< 0.01) and palliated patients had significantly lower average costs per day of admission ($1022, SD=$441) compared to those who were not palliated ($831; SD=$ 1041) (p< 0.001). Discussion: Our study indicates there is a difference between dying patients who received palliative care compared to those who did not in an acute care setting. Further research into the outcomes of patients discharged back to Aged Care facilities for palliative care warrants investigation