DISEASE BASED OUTPUT MEASURES FOR HOSPITALS
Figure 1: Proportion of hospital spending by care type, based on average hospital expenditure from 2015-16 to 2016-17 (footnote 3).
2.2. Output measures
When discussing hospital output it is useful to consider health care output in the broader context of health care. Here output refers to output quantity or volume. It is conceptually preferable to measure health care output on a basis of complete treatment rather than specific types of goods and services going into the treatment (Triplett 2001; National Research Council 2010; Aizorbe and Highfill 2015; OECD, Eurostat and World Health Organisation 2017).
One advantage of redefining output based on treatment of disease is that substitutions across different types of service or health care providers, such as a shift to less invasive treatments, or from inpatient to outpatient care, can be accounted for (Aizorbe and Highfill 2015; Groshen et al 2017). A second advantage is that it is more meaningful to discuss quality changes, especially those reflected as improvement in treatment outcome, on a disease basis than focusing on specific type of goods and services (Hall and Highfill 2014). For example, measuring quality changes may require outcome information on treatment for specific types of disease. While there are good reasons for full disease based measurement, in practice, it is more realistic to measure output on a partial disease basis, limited to broader categories of providers, such as hospitals versus non-hospital providers (footnote 4).
Based on this concept, it is desirable to measure hospital output based on treatment of disease across different types of care (admitted, non-admitted and ED). A disease based measure can capture shifts across different types of treatments for the same disease at hospitals, or shifts across different types of care, e.g. from inpatient to outpatient care (i.e. non-admitted patient care). Calculation of output measures on a disease basis across three different types of care can prove to be challenging in the Australian case due to considerable challenges in mapping non-admitted care and ED data to disease. The non-admitted care and ED data use different classifications and may not contain the same level of detailed clinical information as admitted care. Therefore this work focuses on admitted patient care.
In Australia, hospital data are routinely collected by state and territory health authorities. Relevant data sources include Independent Hospital Pricing Authority (IHPA) National Hospital Cost Data Collection (NHCDC) and AIHW National Hospital Morbidity Database (NHMD). NHCDC contains episode level costs and activity for covering all types of hospital care and is used for producing Australian Refined Diagnosis Related Groups (AR-DRG) cost weights. NHMD contains episode level records of admitted patient care at both public and private hospitals. Disease based analysis requires that cost of treatment be mapped to disease, which is usually done using the episode level data (e.g. AIHW 2017).
In this study, the summary statistics of hospital expenditure on admitted patient care was used, as the episode level data is not readily accessible. The summary data was produced by AIHW, with expenditure being mapped to disease coded in the International Classification of Diseases (ICD) at the Chapter level by age groups (5 year age groups). (ICD Chapters are the broadest categories of diseases.) The data includes both public and private hospitals, from 2004-5 to 2012-13. The data contains two sets of cost allocation to diagnoses. The first set is allocation of expenditure to principal diagnoses only. The second set is allocation of expenditure by splitting it across all cost-relevant diagnoses equally including principal diagnoses, as a way of including comorbidity (AIHW, 2017). Cost-relevant diagnoses are referred to diagnoses that contribute to the total treatment cost (footnote 5). The data uses the Australian Modification of the 10th Revision of ICD (ICD-10-AM), 8th Edition and the data item description is provided in Table A1 in Appendix.
In addition, hospital separation statistics by principal diagnoses from the AIHW Principal Diagnosis Data Cubes was used for constructing quantity indexes of ICD Chapters by age group from the ICD subchapter level. The data was also used for updating the number of separations in the expenditure data to 2014-15.
3. METHOD FOR CONSTRUCTING A QUANTITY INDEX
In constructing a quantity index, stratification was used for controlling for heterogeneity in treatment of disease and in patient characteristics. In theory, episodes of treatment should be grouped by disease by patient characteristics at sufficiently detailed levels so that products in each group can be considered as being approximately similar. However, the level of detail where diseases and patient characteristics can be grouped is largely determined by data availability, as in the case considered here. In the expenditure data used here diseases are categorised broadly by ICD Chapters, with patient characteristics being differentiated by age groups. Note that age is one of the most important characteristics, e.g. patient's comorbidity tends to depend on age. Gender is also a relevant characteristic of patients but it was not included in the data. It is reasonable to assume that differentiation between genders can be partially reflected in some ICD codes (though there can still be a difference in comorbidity.)
Although data on average length of stay (ALOS) (footnote 6) is available from the Principal Diagnosis Data Cubes, this is not used as a defining characteristic for grouping episodes. This means that different patient days for the same disease may be put in the same group. As a result, new or improved treatments or better care that reduce patient days and overall treatment cost can be reflected as price effects rather than reduction in volume (Schreyer 2010; Diewert 2017). A further reason for not grouping episodes by length of stay is that length of stay may implicitly depend on age groups and patient’s comorbidity (as noted above, comorbidity tends to depend on age). Patients in the older age groups or patients with comorbidities tend to require a longer stay in hospitals. However, average length of stay could be used as proxy weights, e.g. for ICD diseases at a lower level of detail when the cost information is not available. For example, average length of stay can be used as proxy weights for ICD subchapters by age group when cost weights are not available (see discussion below and footnote 7).
Quantity indexes by ICD Chapter were calculated as an annual chained Laspeyres index using two different methods. In both cases, the cost allocation to principal diagnosis provided in the expenditure data was used.
The first method uses the number of separations of each diagnosis-age group at the Chapter level as the lowest level indexes. There are about 350 diagnosis-age groups at this level. These unit value indexes are aggregated (across age groups) to the indexes for ICD Chapters and the total quantity index using expenditure weights.
The second method is to construct quantity indexes for ICD Chapters from diagnosis-age groups at a greater level of detail, i.e. the subchapter level. There are around 4000 diagnosis-age groups at this level. As subchapter expenditure weights are not available, average length of stay is used as proxy weights. The aggregation involves two stages. The first stage is to aggregate subchapter level groups to quantity indexes of Chapter for each age group using average length of stay as weights. The second stage is to aggregate these indexes to the Chapter indexes and total index using the expenditure weights as in the first method.
One complication in stratification by disease by patient characteristics is comorbidity. Although the data includes alternative allocation of cost to cost-relevant diagnoses, it assumes equal weights among the relevant diagnoses. Due to lack of well-accepted methods for including comorbidity, allocation of cost to principal diagnoses is assumed here.
It is worth commenting that AR-DRG can be an alternative option for grouping treatments of diseases. In Australia AR-DRG is used for grouping patients in admitted patient care. DRGs are structured by broad categories of medical, surgical, and other. The main advantage is that published DRG weights are available for admitted acute care. However, DRG groups patients based on their similar resource use, strictly speaking not by disease. For example, patients with different principal diagnoses but with similar resource use can appear under the same DRG. Treatment of the same type of disease may fall under different categories, such as medical versus surgical treatments. Based on these considerations, stratification by ICD of diagnoses is preferred over DRG. AR-DRG can be useful for differentiating patient severity in grouping episodes of care. However, this information is not included in the summary data used here.
4. RESULTS AND DISCUSSION
4.1. The results
Table 1: Hospital admitted patient care quantity indexes by disease. (Index 2011-12 = 100.)
Figure 2: Quantity indexes for hospital admitted patient care. (Index 2004-05 = 100.)
Figure 3: Price indexes for hospital admitted patient care. (Index 2004-05 = 100.)
Figure 4: Comparison between public and private hospitals.
Figure 5: Comparison of percentage movements assuming substitution and no substitution between public and private hospitals.
The disease based quantity index can be used in the National Accounts for estimating real output and productivity of hospital services. The quantity index can be used directly for estimating changes in output volume measure on the assumption that the index movement is representative of overall hospital output. Preferably, the quantity index can be used for constructing a deflator or implicit price index which is then used for deriving volume measure by deflation. For example, the quantity index can be used with nominal expenditure, say for admitted care, to create a price index, as in Figure 3 - which is then used as a proxy price measure for total output of hospitals to derive its volume measure.
As the expenditure data only has ICD Chapter level disease breakdowns, this could have some limitations on capturing a prevalence pattern shift, i.e. compositional changes between different diseases within ICD Chapters. For this reason, the second method as discussed above is preferred, although this could be improved using data with expenditure allocated to a greater level of detail in ICD.
Note there are some limitations for using numbers of separations as a basic unit of quantity. There are cases where multiple separations within a defined period (e.g. financial year) may actually form part of treatment for the same disease, where it should be counted as one treatment. There is a similar issue with readmission being accounted as a separate treatment, should be in some cases considered as part of the one treatment. Nonetheless, as the majority of separations are single episodes, use of numbers of separations as a counting unit can be considered a reasonable approximation.
As the present approach does not include non-admitted patient care, it does not capture substitutions between inpatient care and outpatient care. For example, due to advance in medical technology, a treatment that traditionally requires admitted care can now be done at a clinic in a hospital as outpatient. Future work could include both non-admitted patient care and ED presentations when relevant data become available. Non-admitted care data usually contain some clinical information that allows disease mapping. From 2011-12, hospital ED data collection started to include clinical information that can be used for disease mapping. As a result, in principle, it is possible to construct a disease based output measure. However, disease mapping for both non-admitted care and ED services remains challenging due to different classification systems being used in data collection. This could be an area for future development.
It is worth emphasising that although a disease based approach can capture some aspects of quality changes reflected as compositional shifts or substitutions, explicit quality adjustment is required to account for quality change reflected as a change in treatment outcomes. However, development of explicit quality adjustment is a major challenge for National Statistical Offices (NSOs), due to a lack of both a well-accepted conceptual framework and relevant data for measuring changes in treatment outcomes. For further discussion on explicit quality adjustment and the ABS preferred approach, see the recent discussion paper (footnote 2).
The key feature of the disease based approach used here is that products are grouped by diagnoses and patient characteristics. This has the advantage that the effects of substitutions across different types of treatments, driven by advancements in medical technology and improved care practice, are reflected in output measures.
As admitted care is the dominant component of hospital services in terms of hospital expenditure, the quantity index for admitted care can be used for constructing a deflator or implicit price index for hospital services. The price index can be used for estimating changes in real output of all hospital services. Future work could include non-admitted patient care and ED in the output quantity index.
As a disease based quantity index or price index has breakdowns by disease type, it is useful for disease based analysis of drivers for growth in health expenditure, such as identifying types of disease that contribute to the rising health expenditure, or determining whether growth in spending for a particular type of disease is due to an increase in the disease prevalence or in volume per patient.
The quantity index may be useful in enhancing productivity measures and providing further insights into the health care industry. The ABS currently does not measure productivity for the non-market sectors (including health care) and has a long-term goal to develop such measure for these non-market sectors. The ABS will continue to explore potential enhancements in this area.
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Australian Bureau of Statistics (ABS), 2018a. Australian System of National Accounts, 2017-18 (Cat. No. 5204.0). Canberra: ABS.
Australian Bureau of Statistics (ABS), 2018b. Private Hospitals, Australia, 2016-17 (Cat. No. 4390.0). Canberra: ABS.
Australian Institute of Health and Welfare (AIHW), 2018. Hospital Resources 2016-17: Australian Hospital Statistics. Health services series no. 86. Cat. No. HSE 205. Canberra: AIHW.
Australian Institute of Health and Welfare (AIHW), 2017. Australian health expenditure - demographics and diseases: Hospital admitted patient expenditure 2004-05 to 2012-13. Health and welfare expenditure series no. 59. Cat. No. HWE 69. Canberra: AIHW.
Bradley, R., Hunjan, J., and Rozental, L. 2015. Experimental Disease Based Price Indexes.
Diewert, W.E. 2017. Productivity measurement in the public sector: Theory and practice.
Dunn, A., Rittmueller, L., and Whitmire, B. 2015. Introducing the new BEA Health Satellite Account. Survey of Current Business 95 (1).
Groshen, E. L., Moyer, B. C., Aizcorbe, A. M., Bradley, R., and Friedman, D. M. 2017. How Government Statistics Adjust for Potential Biases from Quality Change and New Goods in an Age of Digital Technologies: A View from the Trenches. Journal of Economic Perspectives, Vol. 31, p. 187-210.
Hall, A.E. and Highfill, T. 2014. Calculating disease-based medical care expenditure indexes for medicare beneficiaries: A comparison of method and data choices. Bureau of Economic Analysis (BEA) Working Paper. Wahsington DC: BEA.
Independent Hospital Pricing Authority (IHPA). 2017. National Hospital Cost Data Collection, Public Hospitals Cost Report, Round 19 (Financial Year 2014-15), IHPA.
Luo, Q. 2018. Enhancing Output Measures of the Health Care Industry, ABS Chief Economist Series, June 2018
National Research Council, 2010. Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of Their Improvement. Washington, DC; The National Academies Press.
OECD, Eurostat and World Health Organisation, 2017, A System of Health Accounts 2011, revised edition, OECD Publishing, Paris.
Schreyer, P. 2010. Towards Measuring the Volume Output of Education and Health Services: A Handbook. OECD Statistics Working Papers, 2010/02, OECD Publishing, Paris.
Triplett, J. E. 2001. What's different about health? Human repair and car repair in National Accounts and National Health Accounts. in D.M. Cutler and E.R. Berndt (eds.), Medical Care Output and Productivity (National Bureau of Economic Research Studies in Income and Wealth Series, Volume 62, pp. 15-94), Chicago, IL: University of Chicago Press.
2. “Enhancing Output Measures of the Health Care Industry”, ABS Chief Economist Series, June 2018. <back
3. Based on the recurrent expenditure estimate for public hospitals, excluding teaching and research and other that are not allocated by care type (AIHW 2018), and estimate for private hospitals (ABS 2018b). <back
4. The partial disease based approach differs from the US Bureau of Economic Analysis (BEA) and Bureau of Labour Statistics (BLS) full disease based approaches where output is defined across all types of services and health providers including prescribed pharmaceuticals used in treatment of a disease (Dunn, Rittmueller and Whimire 2015; Bradley et al. 2015). <back
5. In the hospital patient record, comorbidity is recorded as additional diagnoses apart from the principal diagnosis. <back
6. Average Length of Stay (ALOS) for a patient cohort is calculated as number of days of hospital stay divided by number of separations. <back
7. ICD subchapters are the next level below the ICD Chapters. For example, ICD Chapter - Diseases of the circulatory system (I00–I99) includes 10 subchapters, such as acute rheumatic fever (I00–I02), chronic rheumatic heart diseases (I05–I09), hypertensive diseases (I10–I15), etc. <back
8. Index for 2014-15 was derived using the expenditure weights in 2012-13. <back
9. The Australian Prudential Regulation Authority (APRA), Private Health Insurance Membership and Coverage September 2018. <back
Table A1 is summary of the AIHW expenditure data used in this analysis. The data is summary statistics of hospital expenditure on admitted patient care for both public and private hospitals, including psychiatric hospitals, in Australia from 2004-05 to 2012-13.