ORCID Profile
0000-0001-7291-8898
Current Organisations
The University of Sydney Menzies Centre for Health Policy
,
Lown Institute
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Publisher: Cold Spring Harbor Laboratory
Date: 18-03-2022
DOI: 10.1101/2022.03.16.22272475
Abstract: 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 s le 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 ex le services in 2016 US Medicare claims for patients aged over 65: knee arthroscopy, spinal fusion procedures and urinalysis. Readers can access the application at kelsey209.shinyapps.io/hsrdefbuilder/ and the code at elsey209/hsrdefbuilder .
Publisher: BMJ
Date: 06-08-2019
DOI: 10.1136/BMJQS-2018-008338
Abstract: To examine 27 low-value procedures, as defined by international recommendations, in New South Wales public hospitals. Analysis of admitted patient data for financial years 2010–2011 to 2016–2017. Number and proportion of episodes identified as low value by two definitions (narrower and broader), associated costs and bed-days, and variation between hospitals in financial year 2016–2017 trends in numbers of low-value episodes from 2010–2011 to 2016–2017. For 27 procedures in 2016–2017, we identified 5079 (narrower definition) to 8855 (broader definition) episodes involving low-value care (11.00%–19.18% of all 46 169 episodes involving these services). These episodes were associated with total inpatient costs of $A49.9 million (narrower) to $A99.3 million (broader), which was 7.4% (narrower) to 14.7% (broader) of the total $A674.6 million costs for all episodes involving these procedures in 2016–2017, and involved 14 348 (narrower) to 29 705 (broader) bed-days. Half the procedures accounted for less than 2% of all low-value episodes identified three of these had no low-value episodes in 2016–2017. The proportion of low-value care varied widely between hospitals. Of the 14 procedures accounting for most low-value care, seven showed decreasing trends from 2010–2011 to 2016–2017, while three (colonoscopy for constipation, endoscopy for dyspepsia, sentinel lymph node biopsy for melanoma in situ) showed increasing trends. Low-value care in this Australian public hospital setting is not common for most of the measured procedures, but colonoscopy for constipation, endoscopy for dyspepsia and sentinel lymph node biopys for melanoma in situ require further investigation and action to reverse increasing trends. The variation between procedures and hospitals may imply different drivers and potential remedies.
Publisher: Public Library of Science (PLoS)
Date: 12-01-2023
DOI: 10.1371/JOURNAL.PONE.0266154
Abstract: 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 s le 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 ex le services in 2016 US Medicare claims for patients aged over 65: knee arthroscopy, spinal fusion procedures and urinalysis. Readers can access the application at kelsey209.shinyapps.io/hsrdefbuilder/ and the code at elsey209/hsrdefbuilder .
Publisher: CSIRO Publishing
Date: 21-04-2022
DOI: 10.1071/AH21316
Abstract: Objective To explore out-of-pocket (OOP) costs within specialties and in idual 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 s le 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 in idual patients.
Publisher: BMJ
Date: 19-08-2017
Publisher: CSIRO Publishing
Date: 2020
DOI: 10.1071/AH19109
Abstract: 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.
Publisher: BMJ
Date: 13-03-2023
Publisher: BMJ
Date: 03-2019
DOI: 10.1136/BMJOPEN-2018-024142
Abstract: To examine the prevalence, costs and trends (2010–2014) for 21 low-value inpatient procedures in a privately insured Australian patient cohort. 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. 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. 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. 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 s le, 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. 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.
Publisher: Springer Science and Business Media LLC
Date: 05-03-2018
Location: Australia
No related grants have been discovered for Kelsey Chalmers.