Sources of specialist physician fee variation: Evidence from Australian health insurance claims data.

This study explores the variation in specialist physician fees and examines whether the variation can be attributed to patient risk factors, variation between physicians, medical specialties, or other factors. We use health insurance claims data from a large private health insurer in Australia. Although Australia has a publicly funded health system that provides universal health coverage, about 44 % of the population holds private health insurance. Specialist physician fees in the private sector are unregulated; physicians can charge any price they want, subject to market forces. We examine the variation in fees using two price measures: total fees charged and out-of- pocket payments. We follow a two-stage method of removing the influence of patient risk factors by computing risk-adjusted prices at patient-level, and aggregating the adjusted prices over all claims made by each physician to arrive at physician-level average prices. In the second stage, we use variance-component models to analyse the variation in the physician-level average prices. We find that patient risk factors account for a small portion of the variance in fees and out-of-pocket payments. Physician-specific variation accounts for the bulk of the vari- ance. The results underscore the importance of understanding physician characteristics in formulating policy efforts to reduce fee variation.

Overcoming silos in health care systems through meso-level organisations - a case study of health reforms in New South Wales, Australia.

Fragmented care delivery is a barrier to improving health system performance worldwide. Investment in meso-level organisations is a potential strategy to improve health system integration, however, its effectiveness remains unclear. In this paper, we provide an overview of key international and Australian integrated care policies. We then describe Collaborative Commissioning - a novel health reform policy to integrate primary and hospital care sectors in New South Wales (NSW), Australia and provide a case study of a model focussed on older person's care. The policy is theorised to achieve greater integration through improved governance (local stakeholders identifying as part of one health system), service delivery (communities perceive new services as preferable to status quo) and incentives (efficiency gains are reinvested locally with progressively higher value care achieved). If effectively implemented at scale, Collaborative Commissioning has potential to improve health system performance in Australia and will be of relevance to similar reform initiatives in other countries.

"It's not a one operation fits all": A qualitative study exploring fee setting and participation in price transparency initiatives amongst medical specialists in the Australian private healthcare sector.

The Australian government, through Medicare, defines the type of medical specialist services it covers and subsidizes, but it does not regulate prices. Specialists in private practice can charge more than the fee listed by Medicare depending on what they feel 'the market will bear'. This can sometimes result in high and unexpected out-of-pocket (OOP) payments for patients. To reduce pricing uncertainty and 'bill shock' faced by consumers, the government introduced a price transparency website in December 2019. It is not clear how effective such a website will be and whether specialists and patients will use it. The aim of this qualitative study was to explore factors influencing how specialists set their fees, and their views on and participation in price transparency initiatives. We conducted 27 semi-structured interviews with surgical specialists. We analysed the data using thematic analysis and responses were mapped to the Theoretical Domains Framework and the Capability, Opportunity, Motivation and Behavior model. We identified several patient, specialist and system-level factors influencing fee setting. Patient-level factors included patient characteristics, circumstance, complexity, and assumptions regarding perceived value of care. Specialist-level factors included perceived experience and skills, ethical considerations, and gendered-behavior. System-level factors included the Australian Medical Association recommended price list, practice costs, and supply and demand factors including perceived competition and practice location. Specialists were opposed to price transparency websites and lacked motivation to participate because of the complexity of fee setting, concerns over unintended consequences, and feelings of frustration they were being singled out. If price transparency websites are to be pursued, specialists' lack of motivation to participate needs to be addressed.

Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data.

Many administrative health data-based studies define patient cohorts using procedure and diagnosis codes. The impact these criteria have on a study's final cohort is not always transparent to co-investigators or other audiences if access to the research data is restricted. We developed a SAS and R Shiny interactive research support tool which generates and displays the diagnosis code summaries associated with a selected medical service or procedure. This allows non-analyst users to interrogate claims data and groupings of reported diagnosis codes. The SAS program uses a tree classifier to find associated diagnosis codes with the service claims compared against a matched, random sample of claims without the service. Claims are grouped based on the overlap of these associated diagnosis codes. The Health Services Research (HSR) Definition Builder Shiny application uses this input to create interactive table and graphics, which updates estimated claim counts of the selected service as users select inclusion and exclusion criteria. This tool can help researchers develop preliminary and shareable definitions for cohorts for administrative health data research. It allows an additional validation step of examining frequency of all diagnosis codes associated with a service, reducing the risk of incorrect included or omitted codes from the final definition. In our results, we explore use of the application on three example services in 2016 US Medicare claims for patients aged over 65: knee arthroscopy, spinal fusion procedures and urinalysis. Readers can access the application at https://kelsey209.shinyapps.io/hsrdefbuilder/ and the code at https://github.com/kelsey209/hsrdefbuilder.

The financial implications of investigating false-positive and true-positive mammograms in a national breast cancer screening program.

Objectives To determine the total annual screening and further-investigation costs of investigating false-positive and true-positive mammograms in the Australian population breast-screening program. Methods This economic analysis used aggregate-level retrospective cohort data of women screened at a breast-screening clinic. Counts and frequencies of each diagnostic workup-sequence recorded were scaled up to national figures and costed by estimating per-patient costs of procedures using screening clinic cost data. Main outcomes and measures estimated were percentage share of total annual screening and further-investigation costs for the Australian population breast-screening program of investigating false-positive and true-positive mammograms. Secondary outcomes determined were average costs of investigating each false-positive and true-positive mammogram. Sensitivity analyses involved recalculating results excluding subgroups of patients below and above the screening age range of 50-74 years. Results Of 8235 patients, the median age was 60.35 years with interquartile range of 54.17-67.17 years. A total of 15.4% (ranging from 13.4 to 15.4% under different scenarios) of total annual screening and further-investigation costs were from investigating false-positive mammograms. This exceeded the share of costs from investigating true-positives (13%). Conclusions We have developed a transparent and non-onerous approach for estimating the costs of false-positive and true-positive mammograms associated with the national breast-screening program. While determining an optimal balance between false-positives and true-positive rates must rely primarily on health outcomes, costs are an important consideration. We recommend that future research adopts and refines similar approaches to facilitate better monitoring of these costs, benchmark against estimates from other screening programs, and support optimal policy development.

Adenotonsillectomy and adenoidectomy in children: The impact of timing of surgery and post-operative outcomes.

AIM: To investigate the impact of adenotonsillectomy (ADT) and adenoidectomy (AD) on child health and evaluated their post-operative complications. METHODS: We included all children aged <16 years undergoing ADT (tonsillectomy ± adenoidectomy) or AD in New South Wales, Australia, 2008-2017. Health information was obtained from administrative hospitalisation data. Rates of post-operative complications and reoperation were evaluated using generalised estimating equations and Kaplan-Meier methods, respectively. RESULTS: Out of 156 500 included children, 112 361 had ADT and 44 139 had AD. Population rates increased during 2008-2017 (ADT: 68-79 per 10 000 children; AD: 25-34 per 10 000), and children were increasingly operated on at a younger age. Overall, 7262 (6.5%) and 1276 (2.9%) children had post-operative complications (mostly haemorrhage), and 4320 (3.8%) and 5394 (12.2%) required reoperation, following ADT and AD, respectively. Complication rates were highest among children aged 0-1 years, lowest for those 2-5 years and increased with age thereafter. Three-year reoperation rates for children aged 0-1 years were 9.0% and 25.9% following ADT and AD, respectively, decreasing thereafter to 0.5% and 2.1% in children aged 12-13 years. CONCLUSIONS: ADT and AD in Australian children have both increased in frequency and are being done at a younger age. Post-operative complications and reoperation rates highlight surgery is not without risk, especially for children under 2 years old. These findings support a more conservative approach to management of upper respiratory symptoms, with surgery reserved for cases where potential benefits are most likely to outweigh harms.

Patterns of specialist out-of-pocket costs for Australian Medicare services: implications for price transparency.

Objective To explore out-of-pocket (OOP) costs within specialties and individual specialists, and use of Medicare Benefits Schedule (MBS) data for potential price transparency initiatives. Methods We conducted a cross-sectional descriptive study of claims for a 10% random sample of Medicare enrolees for out-of-hospital MBS-billed subsequent and initial consultations between 1 January 2014 and 31 December 2014, specific to cardiologist, oncologist and ophthalmologists (with at least 10 patient visits in 2014). Our main outcomes were the number of locations per provider, number of unique OOP consultation costs per provider and provider-location, and the proportion of bulk-billed visits for these visits. Results We studied 970 cardiologists, 913 ophthalmologists and 376 oncologists. At least 67% of specialists across each specialty had at least two practice locations: cardiologists had a median of three (interquartile range [IQR]: 2-4) and ophthalmologists and oncologists both had a median of two (IQR: 1-3). For subsequent consultations, cardiologists had a median of three unique costs per location (IQR: 2-3), whereas ophthalmologists had a median of four unique costs per location (IQR: 3-5). In contrast, oncologists had a median of one unique cost per location (IQR: 1-2) (57.6% of oncologists' provider-locations charged only the bulk-billing amount). Conclusions Specialists have distinct fee lists that can vary based on location. Summary statistics on price transparency websites based on a single amount (like a median or mean OOP charge) might mask substantial variation in costs and lead to bill shock for individual patients.

Developing a How-to-Guide for Health Technology Reassessment: "The HTR Playbook".

BACKGROUND: To develop a knowledge translation (KT) tool that will provide guidance to stakeholders actively planning or considering implementation of a health technology reassessment (HTR) initiative. METHODS: The KT tool is an international and collaborative endeavour between HTR researchers in Canada, Australia, and the United Kingdom. Evidence from a meta-review of documented international HTR experiences and approaches provided the conceptual framing for the KT tool. The purpose, audience, format, and overall scope and content of the tool were established through iterative discussions and consensus. An initial version of the KT tool was beta-tested with an international community of relevant stakeholders (i.e., potential users) at the Health Technology Assessment International 2018 annual meeting. RESULTS: An open access workbook, referred to as the HTR playbook, was developed. As a KT tool, the HTR playbook is intended to simplify the complex HTR planning process by navigating users step-by-step through 6 strategic domains: characteristics of the candidate health technology (The Stats and Projections), stakeholders to engage (The Team), potential facilitators and/or barriers within the policy context (The Playing Field), strategic use of different levers and tools (The Offensive Plays), unintended consequences (The Defensive Plays), and metrics and methods for monitoring and evaluation (Winning the Game). CONCLUSION: The HTR playbook is intended to enhance a user's ability to successfully complete a HTR by helping them systematically consider the different elements and approaches to achieve the right care for the patient population in question.

Rates of Low-Value Service in Australian Public Hospitals and the Association With Patient Insurance Status.

Importance: Low-value services have limited or no benefit to patients. Rates of low-value service in public hospitals may vary by patient insurance status, given that there may be different financial incentives for treatment of privately insured patients. Objective: To assess the variation in rates of 5 low-value services performed in Australian public hospitals according to patient funding status (ie, private or public). Design, Setting, and Participants: This retrospective cross-sectional study analyzed New South Wales public hospital data from January 2013 to June 2018. Patients included in the sample were over age 18 years and eligible to receive low-value services based on diagnoses and concomitant procedures. Data analysis was conducted from June to December 2020. Main Outcomes and Measures: Hospital-specific rates of low-value knee arthroscopic debridement, vertebroplasty for osteoporotic spinal fractures, hyperbaric oxygen therapy, oophorectomy with hysterectomy, and laparoscopic uterine nerve ablation for chronic pelvic pain were measured. For each measure, rates within each public hospital were compared by patient funding status descriptively and using multilevel models. Results: A total of 219 862 inpatients were included in analysis from 58 public hospitals across the 5 measures. A total of 38 365 (22 904 [59.7%] women; 12 448 [32.4%] aged 71-80 years) were eligible for knee arthroscopic debridement for osteoarthritis; 2520 (1924 [76.3%] women; 662 [26.3%] aged 71-80 years), vertebroplasty for osteoporotic spinal fractures; 162 285 (82 046 [50.6%] women; 28 255 [17.4%] aged 61-70 years), hyperbaric oxygen therapy; 15 916 (7126 [44.8%] aged 41-50 years), oophorectomy with hysterectomy; and 776 (327 [42.1%] aged 18-30 years), uterine nerve ablation for chronic pelvic pain. Overall rates of low-value services varied considerably between measures, with the lowest rate for hyperbaric oxygen therapy (0.3 procedures per 1000 inpatients [47 of 158 220 eligible inpatients]) and the highest for vertebroplasty (30.8 procedures per 1000 eligible patients [77 of 2501 eligible inpatients]). There was significant variation in rates between hospitals, with a few outlying hospitals (ie, <10), particularly for knee arthroscopy (range from 1.8 to 21.0 per 1000 eligible patients) and vertebroplasty (range from 13.1 to 70.4 per 1000 eligible patients), with higher numerical rates of low-value services among patients with private insurance than for those without. However, there was no association overall between patient insurance status and low-value services. Overall differences in rates among those with and without private insurance by individual procedure type were not statistically significant. Conclusions and Relevance: There was significant variation in rates of low-value services in public hospitals. While there was no overall association between private insurance and rate of low-value services, private insurance may be associated with low-value service rates in some hospitals. Further exploration of factors specific to local hospitals and practices are needed to reduce this unnecessary care.

Utilization Rates of Pancreatectomy, Radical Prostatectomy, and Nephrectomy in New York, Ontario, and New South Wales, 2011 to 2018.

Importance: Few studies have compared surgical utilization between countries or how rates may differ according to patients' socioeconomic status. Objective: To compare population-level utilization of 3 common nonemergent surgical procedures in New York State (US), Ontario (Canada), and New South Wales (Australia) and how utilization differs for residents of lower- and higher-income neighborhoods. Design, Setting, and Participants: This cohort study included all adults aged 18 years and older who were hospitalized for pancreatectomy, radical prostatectomy, or nephrectomy between 2011 and 2016 in New York, between 2011 and 2018 in Ontario, and between 2013 and 2018 in New South Wales. Each patient's address of residence was linked to 2016 census data to ascertain neighborhood income. Data were analyzed from August 2019 to November 2020. Main Outcomes and Measures: Primary outcomes were (1) each jurisdiction's per capita age- and sex-standardized utilization rates (procedures per 100 000 residents per year) for each surgery and (2) utilization rates among residents of lower- and higher-income neighborhoods. Results: This study included 115 428 surgical patients (25 780 [22.3%] women); 5717, 21 752, and 24 617 patients in New York were hospitalized for pancreatectomy, radical prostatectomy, and nephrectomy, respectively; 4929, 19 125, and 16 916 patients in Ontario, respectively; and 2069, 13 499, and 6804 patients in New South Wales, respectively. Patients in New South Wales were older for all procedures (eg, radical prostatectomy, mean [SD] age in New South Wales, 64.8 [7.3] years; in New York, 62.7 [8.4] years; in Ontario, 62.8 [6.7] years; P < .001); patients in New York were more likely than those in other locations to be women for pancreatectomy (New York: 2926 [51.2%]; Ontario: 2372 [48.1%]; New South Wales, 1003 [48.5%]; P = .004) and nephrectomy (New York: 10 645 [43.2%]; Ontario: 6529 [38.6%]; 2605 [38.3%]; P < .001). With the exception of nephrectomy in Ontario, there was a higher annual utilization rate for all procedures in all jurisdictions among patients residing in affluent neighborhoods (quintile 5) compared with poorer neighborhoods (quintile 1). This difference was largest in New South Wales for pancreatectomy (4.65 additional procedures per 100 000 residents [SE, 0.28]; P < .001) and radical prostatectomy (73.46 additional procedures per 100 000 residents [SE, 1.20]; P < .001); largest in New York for nephrectomy (8.43 additional procedures per 100 000 residents [SE, 0.85]; P < .001) and smallest in New York for radical prostatectomy (19.70 additional procedures per 100 000 residents [SE, 2.63]; P < .001); and smallest in Ontario for pancreatectomy (1.15 additional procedures per 100 000 residents [SE, 0.28]; P < .001) and nephrectomy (-1.10 additional procedures per 100 000 residents [SE, 0.52]; P < .001). New York had the highest utilization of nephrectomy (28.93 procedures per 100 000 residents per year [SE, 0.18]) and New South Wales for had the highest utilization of pancreatectomy and radical prostatectomy (6.94 procedures per 100 000 residents per year [SE, 0.15] and 94.37 procedures per 100 000 residents per year [SE, 0.81], respectively; all P < .001). Utilization was lowest in Ontario for all procedures (pancreatectomy, 6.18 procedures per 100 000 residents per year [SE, 0.09]; radical prostatectomy, 49.24 procedures per 100 000 residents per year [SE, 0.36]; nephrectomy, 21.40 procedures per 100 000 residents per year [SE, 0.16]; all P < .001). Conclusions and Relevance: In this study, New York and New South Wales had higher per capita surgical utilization and larger neighborhood income-utilization gradients than Ontario. These findings suggest that income-based disparities are larger in the United States and Australia and smaller in Canada and highlight trade-offs inherent in the health care systems of different countries.

Assessment of Overuse of Medical Tests and Treatments at US Hospitals Using Medicare Claims.

Importance: Overuse of health care services exposes patients to unnecessary risk of harm and costs. Distinguishing patterns of overuse among hospitals requires hospital-level measures across multiple services. Objective: To describe characteristics of hospitals associated with overuse of health care services in the US. Design, Setting, and Participants: This retrospective cross-sectional analysis used Medicare fee-for-service claims data for beneficiaries older than 65 years from January 1, 2015, to December 31, 2017, with a lookback of 1 year. Inpatient and outpatient services were included, and services offered at specialty and federal hospitals were excluded. Patients were from hospitals with the capacity (based on a claims filter developed for this study) to perform at least 7 of 12 investigated services. Statistical analyses were performed from July 1, 2020, to December 20, 2020. Main Outcomes and Measures: Outcomes of interest were a composite overuse score ranging from 0 (no overuse of services) to 1 (relatively high overuse of services) and characteristics of hospitals clustered by overuse rates. Twelve published low-value service algorithms were applied to the data to find overuse rates for each hospital, normalized and aggregated to a composite score and then compared across 6 hospital characteristics using multivariable regression. A k-means cluster analysis was used on normalized overuse rates to identify hospital clusters. Results: The primary analysis was performed on 2415 cohort A hospitals (ie, hospitals with capacity for 7 or more services), which included 1 263 592 patients (mean [SD] age, 72.4 [14] years; 678 549 women [53.7%]; 101 017 191 White patients [80.5%]). Head imaging for syncope was the highest-volume low-value service (377 745 patients [29.9%]), followed by coronary artery stenting for stable coronary disease (199 579 [15.8%]). The mean (SD) composite overuse score was 0.40 (0.10) points. Southern hospitals had a higher mean score than midwestern (difference in means: 0.06 [95% CI, 0.05-0.07] points; P < .001), northeast (0.08 [95% CI, 0.06-0.09] points; P < .001), and western hospitals (0.08 [95% CI, 0.07-0.10] points; P < .001). Nonprofit hospitals had a lower adjusted mean score than for-profit hospitals (-0.03 [95% CI, -0.04 to -0.02] points; P < .001). Major teaching hospitals had significantly lower adjusted mean overuse scores vs minor teaching hospitals (difference in means, -0.07 [95% CI, -0.08 to -0.06] points; P < .001) and nonteaching hospitals (-0.10 [95% CI, -0.12 to -0.09] points; P < .001). Of the 4 clusters identified, 1 was characterized by its low counts of overuse in all services except for spinal fusion; the majority of major teaching hospitals were in this cluster (164 of 223 major teaching hospitals [73.5%]). Conclusions and Relevance: This cross-sectional study used a novel measurement of hospital-associated overuse; results showed that the highest scores in this Medicare population were associated with nonteaching and for-profit hospitals, particularly in the South.

To Expand the Evidence Base About Harms from Tests and Treatments.

Rigorous evidence about the broad range of harms that might be experienced by a patient in the course of testing and treatment is sparse. We aimed to generate recommendations for how researchers might more comprehensively evaluate potential harms of healthcare interventions, to allow clinicians and patients to better include this evidence in clinical decision-making. We propose seven domains of harms of tests and treatments that are relevant to patients: (1) physical impairment, (2) psychological distress, (3) social disruption, (4) disruption in connection to healthcare, (5) labeling, (6) financial impact, and (7) treatment burden. These domains will include a range of severity of harms and variation in timing after testing or treatment, attributable to the service itself or a resulting care cascade. Although some new measures may be needed, diverse data and tools are available to allow the assessment of harms comprehensively across these domains. We encourage researchers to evaluate harms in sub-populations, since the harms experienced may differ importantly by demographics, social determinants, presence of comorbid illness, psychological state, and other characteristics. Regulators, funders, and editors might require either assessment or reporting of harms in each domain or require justification for inclusion and exclusion of different domains.

Adverse Events and Hospital-Acquired Conditions Associated With Potential Low-Value Care in Medicare Beneficiaries.

Importance: There has been insufficient research on the patient harms and costs associated with potential low-value procedures in the US Medicare population. Objective: To report the prevalence of adverse events associated with potential low-value procedures and the additional hospital length of stay (LOS) and costs. Design Setting and Participants: This is a retrospective cohort study using Medicare fee-for-service claims between January 2016 to December 2018. Participants were aged 65 years or older. Procedures were selected if they had previously published indicators of low-value care, including knee arthroscopy, spinal fusion, vertebroplasty, percutaneous coronary intervention (PCI), carotid endarterectomy, renal stenting, and hysterectomy for benign conditions. Analysis was conducted from July to December, 2020. Main Outcomes and Measures: For inpatient procedures, the number and rate of admissions with a hospital-acquired condition (HAC) or patient safety indicator event (PSIs), as well as the unadjusted and adjusted difference in mean LOS and Medicare costs between admissions with and without a HAC/PSI. For outpatient procedures, we report the number of claims where the beneficiary had an unplanned hospital admission within seven days and the number of these admissions with a HAC/PSI. Results: There were 573 351 patients included in the study, with 617 264 procedures; the mean (SD) age was 74.2 (6.7) years, with 320 637 women (55.9%), and mostly White patients (520 735; 90.8%). Among the 197 755 claims for the inpatient procedures, 231 had an HAC and 1764 had a PSI. Spinal fusion was associated with the most HACs (123 admissions) and PSIs (1015 admissions). Overall, HACs during a PCI admission were associated with the highest adjusted additional mean LOS (17.5 days; 95% CI, 10.3-23.6), with also the highest adjusted additional mean cost ($22 000; 95% CI, $9100-$32 600). There were 419 509 included outpatient procedures, and 7514 (1.8%) had an unplanned admission within 7 days. A total of 17 HACs and PSIs occurred in these admissions. Conclusions and Relevance: In this cross-sectional cohort study of Medicare fee-for-service claims, patients receiving potential low-value care were exposed to risk of unnecessary harm associated with higher cost and LOS.

Hospital characteristics associated with low-value care in public hospitals in New South Wales, Australia.

BACKGROUND: Rates of low-value care vary between hospitals in New South Wales, Australia. Understanding factors associated with this variation will help in understanding the drivers of low-value care and in planning initiatives to reduce low-value care. METHODS: For eight low-value procedures, we used Poisson regression of the number of low-value episodes at each hospital to assess the association between low-value care and hospital characteristics. We also used hierarchical clustering on the low-value procedures used and their rates at each hospital to try to identify groups of hospitals with higher or lower rates of low-value care across multiple procedures. RESULTS: Some hospital characteristics, such as hospital peer group and proportion of total episodes that involve the specific procedure, showed associations for some procedures, but none were consistent across all eight procedures. We clustered hospitals into five groups, but low-value care rates did not differ much between these groups. CONCLUSION: Available hospital variables show little association with rates of low-value care and no patterns across different low-value procedures. We need to investigate factors within hospitals, such as clinician knowledge and beliefs about low-value care.

Estimating misclassification error in a binary performance indicator: case study of low value care in Australian hospitals.

OBJECTIVE: Indicators based on hospital administrative data have potential for misclassification error, especially if they rely on clinical detail that may not be well recorded in the data. We applied an approach using modified logistic regression models to assess the misclassification (false-positive and false-negative) rates of low-value care indicators. DESIGN AND SETTING: We applied indicators involving 19 procedures to an extract from the New South Wales Admitted Patient Data Collection (1 January 2012 to 30 June 2015) to label episodes as low value. We fit four models (no misclassification, false-positive only, false-negative only, both false-positive and false-negative) for each indicator to estimate misclassification rates and used the posterior probabilities of the models to assess which model fit best. RESULTS: False-positive rates were low for most indicators-if the indicator labels care as low value, the care is most likely truly low value according to the relevant recommendation. False-negative rates were much higher but were poorly estimated (wide credible intervals). For most indicators, the models allowing no misclassification or allowing false-negatives but no false-positives had the highest posterior probability. The overall low-value care rate from the indicators was 12%. After adjusting for the estimated misclassification rates from the highest probability models, this increased to 35%. CONCLUSION: Binary performance indicators have a potential for misclassification error, especially if they depend on clinical information extracted from administrative data. Indicators should be validated by chart review, but this is resource-intensive and costly. The modelling approach presented here can be used as an initial validation step to identify and revise indicators that may have issues before continuing to a full chart review validation.

First steps towards price transparency: comparability of online out-of-pocket tools from Australian private health funds.

Objective The aims of this study were to compare and contrast the information three Australian private health insurance funds (HCF, Bupa and Medibank) have provided on their online out-of-pocket cost tools and to consider the implications this has for price transparency in Australia. Methods Website data were downloaded from HCF, Bupa and Medibank on 18 February 2019. The information and statistics provided on these pages were reviewed, and the procedures compared across funds if their pages had referred to the same Medicare Benefits Schedule (MBS) item(s). Information was extracted regarding descriptions of the claims data used, the types of statistics provided, the out-of-pocket estimates, the total procedure cost, the MBS items referenced and the assumptions the funds described on their pages. Results HCF specified the MBS items used to select the claims data for their estimates, whereas Bupa and Medibank only referred to common MBS items associated with the procedures. On average, HCF had 1.44 more MBS items listed than Bupa and 2.08 more than Medibank. The funds organised procedures differently, such as HCF providing separate cost estimates for vaginal, abdominal and keyhole hysterectomy compared with Medibank's single estimate for hysterectomy costs. Conclusions These funds have started to address the need for transparent out-of-pocket cost information, but the differences across these pages demonstrate complexities and the potential obfuscation of cost data. What is known about the topic? Out-of-pocket costs are highly variable and patient 'bill shock' is an increasing concern in Australia. Private insurance funds have created online tools to share procedure cost estimates based on their claims data. What does this paper add? This is the first review of Australian insurance funds' price transparency tools. The cost information is difficult to interpret both within funds (for members) and across funds (for the system). What are the implications for practitioners? Policy makers will need to consider the complexities and presentation options for cost estimates within the health system if they move ahead with a public price transparency tool. There is still a requirement for cost information that can facilitate price shopping across providers and funders.