Jabaar, R

Mosendane, T

Persadh, A S

Ruff, B

BMC Health Services Research

English

PubMed

T Mosendane

A S Persadh

R Jabaar

B Ruff

Crossref

MEETING ABSTRACT Open AccessUtilization of clinical tools to understandhealth-care differences of the DiscoveryHealth populationT Mosendane*, A S Persadh, R Jabaar, B RuffFrom 26th Patient Classification Systems International (PCSI) Working ConferenceMunich, Germany. 15-18 September 2010IntroductionVarious clinical tools exist that categorize health-caredata into homogenous cohorts. The tools contribute tothe understanding of health-care experiences. The toolsdescribed below are used in understanding the Discov-ery Health population.At a patient level, the John Hopkins Adjusted ClinicalGroups (ACG) case-mix system identifies common com-binations of disease that impact on each other toincrease an individual’s risk. The tool is thus robust notonly in categorizing patient illness, but also in analyzinghealth-care utilization and cost.At an episode of care level, the Discovery Episodegrouper (DEG) uses ICD-10 coded data to define epi-sodes. These episodes provide a means for assessinghealth-care quality and cost.At the hospital level the Diagnosis Related Group(DRG), a patient classification system, provides anunderstanding of the costs incurred by measuring thecase-mix index of a hospital.Using the ACG, DEG and DRG, Discovery Health datacan be segregated into various resource bands. High-cost members can be identified, and the costs incurredby different providers assessed.Studies done in various regions in the United Stateshave shown that Medicare spending varies in differentareas across the country. Stephan Zuckerman explainsthat “geographic differences in Medicare spending arenot necessarily evidence of inefficiency in health care,”and that “29% of the differences can be attributed tohealth, but 33% remains unexplained”.Using region as a starting point, the aim of this pro-ject is to understand how much of the differences inhealth-care cost identified can be explained using theclinical tools. This explainable portion is due to the dif-ference in population demographics and disease burdenwithin different regions. Possible explanatory factors forthe residual differences will be explored, with regionaldifferences forming the starting point of the study.MethodsAssess and understand the disease burden in variousregions by categorizing members into different resourceutilization bands (RUBs) using the ACGs.Identify diseases that exist within the RUBs using theDRGs for hospital cases, DEG for separate episodes, andthe Discovery Health Label Matrix for a combined epi-sode view.Adjust for differences in disease burden and episodesof care in the different provinces, define other factorscontributing to differences in health-care costs, leavingan unexplained inefficiency/efficiency cost per provinceto be explained.ResultsRUB distributions were as follows: Free State had thehighest number of RUB 5 members (2.3%), followed byGauteng (1.5%), Mpumalanga and North West (1.4%),Limpopo, Northern Cape and Eastern Cape (1.3%),KwaZulu Natal (1.2%) and Western Cape (1.1%).The distribution of RUB 4 members in the countrywas 7.5% in the Free State, 7.3% in Gauteng, 6.2% inMpumalanga, 6% in Eastern Cape, 5.9% in North Westand Northern Cape, 5.8% in KwaZulu Natal and Lim-popo and 5.4% in the Western Cape.Clinical Risk Management, Health Insurance, Johannesburg, Gauteng, SouthAfricaMosendane et al. BMC Health Services Research 2010, 10(Suppl 2):A18http://www.biomedcentral.com/1472-6963/10/S2/A18© 2010 Mosendane et al; licensee BioMed Central Ltd.The majority of RUB 3 members are found inGauteng (42.6%), with Eastern Cape (38.8%), Free State(38.6%), Mpumalanga (37.0%), KwaZulu Natal (36.2%),Western Cape (35.8%), North West (34.4%), NorthernCape (34.1%) and Limpopo (33.7%) following.RUB 2 users were distributed in Mpumalanga (22.3%),North West (22.2%), Gauteng (21.4%), Limpopo (21.2%),Eastern Cape (21.1%), KwaZulu Natal (21.0%), WesternCape (20.8%,), Northern Cape (20.1%) and Free State(19.9%).Members who were considered to be healthy usersand categorized into RUB 1 were 17.0% in the WesternCape, 16.8% in the Eastern Cape and KwaZulu Natal,16.5 % in the Northern Cape, 16.2% in Limpopo, 15.9%in Mpumalanga, 15.5% in North West, 15.4% in Gau-teng and 14.5% in the Free State. The highest numbersof non-users were found in Northern Cape (22.2%).Limpopo had 21.8% and North West had 21.0% of non-users. In Western Cape and KwaZulu Natal, 19.8% and19.0% of non-users were found, respectively. Just over17% of non-users were in Mpumalanga (17.3%) and FreeState (17.2%), and the lowest number of non-users wasdistributed with Gauteng (11.8%).After adjusting for the different RUB distribution ofpatients in different provinces, costs for both Gautengand Free State were higher than that predicted by 4%and 2%, respectively. The costs for Gauteng and FreeState were also above the average cost due to the highproportion of RUB 4 and RUB 5 members. Furtherinvestigation is required to understand this residual.ConclusionsThere are distinct geographical differences within SouthAfrica, with higher costs incurred in higher-cost areas.Further analyses are underway to obtain a greaterunderstanding of other factors that may be contributingto the unexplained differences in health-care costsexperienced by different provinces.Published: 6 October 2010doi:10.1186/1472-6963-10-S2-A18Cite this article as: Mosendane et al.: Utilization of clinical tools tounderstand health-care differences of the Discovery Health population.BMC Health Services Research 2010 10(Suppl 2):A18.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitMosendane et al. BMC Health Services Research 2010, 10(Suppl 2):A18http://www.biomedcentral.com/1472-6963/10/S2/A18Page 2 of 2

Utilization of clinical tools to understand health-care differences of the Discovery Health population

Springer - Publisher Connector

MEETING ABSTRACT Open Access
Utilization of clinical tools to understand
health-care differences of the Discovery
Health population
T Mosendane*, A S Persadh, R Jabaar, B Ruff
From 26th Patient Classification Systems International (PCSI) Working Conference
Munich, Germany. 15-18 September 2010
Introduction
Various clinical tools exist that categorize health-care
data into homogenous cohorts. The tools contribute to
the understanding of health-care experiences. The tools
described below are used in understanding the Discov-
ery Health population.
At a patient level, the John Hopkins Adjusted Clinical
Groups (ACG) case-mix system identifies common com-
binations of disease that impact on each other to
increase an individual’s risk. The tool is thus robust not
only in categorizing patient illness, but also in analyzing
health-care utilization and cost.
At an episode of care level, the Discovery Episode
grouper (DEG) uses ICD-10 coded data to define epi-
sodes. These episodes provide a means for assessing
health-care quality and cost.
At the hospital level the Diagnosis Related Group
(DRG), a patient classification system, provides an
understanding of the costs incurred by measuring the
case-mix index of a hospital.
Using the ACG, DEG and DRG, Discovery Health data
can be segregated into various resource bands. High-
cost members can be identified, and the costs incurred
by different providers assessed.
Studies done in various regions in the United States
have shown that Medicare spending varies in different
areas across the country. Stephan Zuckerman explains
that “geographic differences in Medicare spending are
not necessarily evidence of inefficiency in health care,”
and that “29% of the differences can be attributed to
health, but 33% remains unexplained”.
Using region as a starting point, the aim of this pro-
ject is to understand how much of the differences in
health-care cost identified can be explained using the
clinical tools. This explainable portion is due to the dif-
ference in population demographics and disease burden
within different regions. Possible explanatory factors for
the residual differences will be explored, with regional
differences forming the starting point of the study.
Methods
Assess and understand the disease burden in various
regions by categorizing members into different resource
utilization bands (RUBs) using the ACGs.
Identify diseases that exist within the RUBs using the
DRGs for hospital cases, DEG for separate episodes, and
the Discovery Health Label Matrix for a combined epi-
sode view.
Adjust for differences in disease burden and episodes
of care in the different provinces, define other factors
contributing to differences in health-care costs, leaving
an unexplained inefficiency/efficiency cost per province
to be explained.
Results
RUB distributions were as follows: Free State had the
highest number of RUB 5 members (2.3%), followed by
Gauteng (1.5%), Mpumalanga and North West (1.4%),
Limpopo, Northern Cape and Eastern Cape (1.3%),
KwaZulu Natal (1.2%) and Western Cape (1.1%).
The distribution of RUB 4 members in the country
was 7.5% in the Free State, 7.3% in Gauteng, 6.2% in
Mpumalanga, 6% in Eastern Cape, 5.9% in North West
and Northern Cape, 5.8% in KwaZulu Natal and Lim-
popo and 5.4% in the Western Cape.Clinical Risk Management, Health Insurance, Johannesburg, Gauteng, South
Africa
Mosendane et al. BMC Health Services Research 2010, 10(Suppl 2):A18
http://www.biomedcentral.com/1472-6963/10/S2/A18
© 2010 Mosendane et al; licensee BioMed Central Ltd.
The majority of RUB 3 members are found in
Gauteng (42.6%), with Eastern Cape (38.8%), Free State
(38.6%), Mpumalanga (37.0%), KwaZulu Natal (36.2%),
Western Cape (35.8%), North West (34.4%), Northern
Cape (34.1%) and Limpopo (33.7%) following.
RUB 2 users were distributed in Mpumalanga (22.3%),
North West (22.2%), Gauteng (21.4%), Limpopo (21.2%),
Eastern Cape (21.1%), KwaZulu Natal (21.0%), Western
Cape (20.8%,), Northern Cape (20.1%) and Free State
(19.9%).
Members who were considered to be healthy users
and categorized into RUB 1 were 17.0% in the Western
Cape, 16.8% in the Eastern Cape and KwaZulu Natal,
16.5 % in the Northern Cape, 16.2% in Limpopo, 15.9%
in Mpumalanga, 15.5% in North West, 15.4% in Gau-
teng and 14.5% in the Free State. The highest numbers
of non-users were found in Northern Cape (22.2%).
Limpopo had 21.8% and North West had 21.0% of non-
users. In Western Cape and KwaZulu Natal, 19.8% and
19.0% of non-users were found, respectively. Just over
17% of non-users were in Mpumalanga (17.3%) and Free
State (17.2%), and the lowest number of non-users was
distributed with Gauteng (11.8%).
After adjusting for the different RUB distribution of
patients in different provinces, costs for both Gauteng
and Free State were higher than that predicted by 4%
and 2%, respectively. The costs for Gauteng and Free
State were also above the average cost due to the high
proportion of RUB 4 and RUB 5 members. Further
investigation is required to understand this residual.
Conclusions
There are distinct geographical differences within South
Africa, with higher costs incurred in higher-cost areas.
Further analyses are underway to obtain a greater
understanding of other factors that may be contributing
to the unexplained differences in health-care costs
experienced by different provinces.
Published: 6 October 2010
doi:10.1186/1472-6963-10-S2-A18
Cite this article as: Mosendane et al.: Utilization of clinical tools to
understand health-care differences of the Discovery Health population.
BMC Health Services Research 2010 10(Suppl 2):A18.
Submit your next manuscript to BioMed Central
and take full advantage of: 
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at 
www.biomedcentral.com/submit
Mosendane et al. BMC Health Services Research 2010, 10(Suppl 2):A18
http://www.biomedcentral.com/1472-6963/10/S2/A18
Page 2 of 2


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