PRESCRIBED MINIMUM BENEFITS
CODES AND RELATIVE VALUE UNITS
Abt Associates South Africa Inc.
16th Floor, the Forum
2 Maude St
Sandton
2146
Tel : 011-883-7547
Fax: 011-883-6790
E-mail : neil_soderlund@abtassoc.co.za
Report prepared for the Board of
Healthcare Funders
by
Dr Neil Söderlund
Dr Shaun Conway
Abt Associates Inc
Contents
1.
INTERPRETATION OF DATABASE ...................................................................................... 3
2.
INSTRUCTIONS FOR USE OF THE DATABASE: ................................................................ 5
3.
RELATIVE VALUE UNITS (RVUS) AND THE SUITABILITY OF MINIMUM
BENEFITS CODES FOR REIMBURSEMENT PURPOSES............................................................ 6
3.1 CALCULATING RVUS ................................................................................................................. 6
3.2 RESOURCE PREDICTIVE POWER ................................................................................................... 7
3.2.1
Data sources ..................................................................................................................... 7
3.2.2
Statistical methods ............................................................................................................ 7
3.2.3
Results............................................................................................................................... 9
3.2.4
Conclusions ...................................................................................................................... 9
4.
REFERENCES ............................................................................................................................ 11
1. Interpretation of database
The recently published Schedule of Prescribed Minimum Benefits requires that all
Medical Schemes provide for a set of cost-effective and urgently required medical
interventions. Although these are listed as approximately 300 categories in the
regulations, there is in many cases, not sufficient detail to accurately map these
categories onto routine health information systems for the purposes of preauthorisation, product pricing, and retrospective review. Furthermore, the schedule
gives no information on the additional 300 or so categories which contain cases
excluded from the schedule. Consequently, the Board of Healthcare Funders has
commissioned Abt Associates to translate the Minimum Benefits Schedule (MBS)
into coded format for ease of identification of included and excluded diagnoses.
For each minimum benefit category (both those included in the schedule, and those
considered, but excluded from the schedule), the following are provided:
1. The Minimum Benefit Code, diagnosis, and treatment descriptions for each
Minimum Benefits Category (MBC) and body system chapter within which it is
located.
2. Whether or not the MBC concerned is included in the Minimum Benefits
Schedule.
3. ICD-10 diagnosis codes that map to the MBC. This is a many-to-many mapping.
Only valid primary diagnosis codes were considered, thus excluding most
‘external cause’, ‘tumour morpology’ and ‘reason for treatment’ codes (i.e. ‘U’ to
‘Z’ codes).
4. Surgical procedure codes (CPT-4 and Gazette codes) that would be appropriate
for each MBC. This is a many-to-many mapping.
5. Special procedures of a non-surgical nature that are specifically detailed in the
MBS - mapped to MBC.
6. A free text field noting additional factors that may need to be considered in
assessing whether a diagnosis is covered by the MBS or not.
7. Relative Value Units for each MBC (one-to-one mapping).
Not all diagnoses within ICD-10 could be mapped to a MBC. Consequently two
catch-all categories were included:
991Z - MISCELLANEOUS EXCLUDED - diagnoses were allocated to this category
under the following conditions :
1. A number of additional categories were necessary to categorise all ICD10 codes,
some of which did not exist under ICD-9, and were thus not included in the
original MBCs.
2. Some diagnoses within ICD-10 were deemed insufficiently specific to be
allocated to the primary field. For example, the ICD-10 code: S899 UNSPECIFIED INJURY OF LOWER LEG was considered insufficiently
specific to justify mandatory coverage. Consequently, many of the
‘.....UNSPECIFIED’ or ‘.....CLASSIFIED ELSEWHERE’ diagnoses are allocated
to this category. In other cases, they describe a symptom or the result of a
laboratory test, rather than a diagnosis eg. HEADACHE. In these cases, an
attempt should be made to get a more specific diagnosis from the attending doctor.
3. A further set of diagnoses did not map to any of the Minimum Benefit Categories,
and, in the opinion of the researchers, were not severe enough to qualify for
inclusion in terms of the general objectives of the MBS.
4. Some diagnoses are untreatable by definition and thus should be excluded from
the package - these include all cases where the patient is already dead, and
congenital abnormalities incompatible with life.
990Z - OTHER MISCELLANEOUS INCLUDED DIAGNOSES. This category
includes diagnoses which are themselves not included within the minimum benefit
schedule, but are typically indicative of an underlying condition which is included
elsewhere in the schedule, or are sequelae of treatment for an included diagnosis. It
is recommended that Medical Schemes provide cover for these conditions at least
until serious underlying pathology has been ruled out.
While we believe that a translation of the schedule into codes will be very valuable
for funders, a number of cautions should be stated at the outset :

Any coded translation of the regulations will not have the force of the regulations
themselves. We have suggested a mapping of diagnosis codes to included and
excluded benefits, but this will hold no force in law. Because of imprecision in
medical terminology, there will never be full agreement about what is actually
included or excluded in the schedule. Medical Schemes are strongly advised to
apply a healthy dose of common sense to the interpretation of the schedule, and the
accompanying database, and should change should change allocations of codes to
categories where they deem this appropriate.
In all cases, when a case is being considered for exclusion of benefit, the
Gazetted Minimum Benefit Schedule should be consulted to assess the
consistency of that particular case with the letter of the regulations.

Although the schedule inclusions are described in terms of both diagnoses and
treatment, in the vast majority of cases, assignment to a minimum benefit category
(MBC) can be made solely on diagnostic information. In reality, a very wide range
of interventions may be justified for a given diagnosis, and specifying all
alternatives is neither possible nor useful. Furthermore, some interventions would
be possible for most diagnoses. For example, almost all diagnoses might require
minor interventions such as the insertion of an intravenous line, resuscitation or a
chest x-ray. Where type of treatment is critical, this tends to be for the special
treatments listed separately at the end of the schedule. For the remainder of cases
requiring surgery, we have instead listed a set of likely surgical procedure codes
derived from the original Oregon categories. These include only true surgical
procedures, thus excluding generic IV insertion, x-ray, anaesthetics, etc, codes.
Surgical procedure codes given are not intended to be comprehensive, and should
not influence the assessment of whether or not a given diagnosis is included within
the MBS or not.

Both procedures and diagnoses are mapped independently onto Minimum
Benefits Categories. Because a range of diagnoses are mixed together within one
category, not all listed procedures will apply to every diagnosis. This is illustrated
for category 950F (GIT cancers) below. While all of the listed diagnoses (to the
left of the box) and procedures (to the right of the box) map to the same category,
GIT cancer, a link from a given diagnosis to any of the procedures cannot be
inferred. For example, a rectal resection could not be considered a reasonable
procedure for cancer of the oesophagus.
Oesophagectomy
Oesophageal
cancer.
Stomach cancer
Cancer of the
ascending colon
CANCER OF THE GIT
INCLUDING OESOPHAGUS,
STOMACH, BOWEL,
RECTUM, ANUS,
TREATABLE
Rectal cancer

Gastrectomy
Colectomy
Rectal resection
The original schedule was derived from categories composed of ICD-9 procedure
codes and CPT-4 diagnosis codes. The cross-map from ICD-9 to ICD-10 is only
approximately 55% complete. That from CPT-4 to South African Gazette codes is
even less complete1. In both cases, this is due principally to intrinsic differences
between the coding systems, rather than defective cross-walks.
For imperfectly
mapping codes ICD-codes, an approximate best fit minimum benefits category was
determined by the authors. Complete hand checking of all procedure code crosswalks to gazette codes was not undertaken because these are not required for
determining whether or not a given case falls into the minimum benefits schedule.
Schemes should feel free to alter these allocations if they disagree with the SAMA
cross-walk used.
2. Instructions for use of the database:
The database has been constructed in Microsoft Access ‘97. A startup form has been
provided to allow searching on ICD10 code, diagnosis, or another field to determine
whether or not a case falls within the MBS. After finding the desired diagnosis, the
user should check the ‘treatment’, ‘special procedures’ and ‘other relevant
information’ boxes to ascertain whether information other than diagnosis needs to be
1
The cross-walks used were obtained from The UK National Casemix Office and the South African
Medical Association respectively.
taken into account when determining inclusion within the MBS. The procedural
information, and the RVUs shown for the category concerned are for information only
and should not be treated as acceptable treatment procedures, or mandated
reimbursement rates. Some diagnosis codes appear more than once in the schedule
because they map to more than one MBC. These are marked as such, and the user
should check all related MBCs.
Many companies will wish to incorporate the tables from the access database into
their own pre-authorisation or other systems. This can be done by simply extracting
the relevant tables, which are labelled fairly intuitively. The Minimum Benefit
Category Code is labelled ‘Newline’ in all of the tables and facilitates linking the
minimum benefit schedule (INCLUSIONS) to the procedures tables (PROC,
SPECIAL PROCEDURES) and the diagnoses table (DIAG_2ND).
3. Relative value units (RVUs) and the suitability of minimum
benefits codes for reimbursement purposes.
In many countries, state and private health insurers have adopted global, diagnosis
based descriptors such as Diagnosis Related Groups (DRGs) for reimbursing
hospitals. This approach considerably reduces the transaction costs of third party
payment, and gives providers the incentives to provide care more efficiently because
reimbursement is determined by the level of patient need, not the service intensity
provided. With this in mind, the BHF requested that a set of relative value units be
constructed which could be used to determine global reimbursement rates for
hospitalisation episodes. This has been done. In addition, we have evaluated the
explanatory power of the MBCs with regard to hospital resource use to assess whether
they are suitable for use as reimbursement tools.
3.1 Calculating RVUs
Data sources
Three sources were used to estimate relative value units for MBCs:

UK NHS hospital data - chosen principally because of the large sample sizes of
ICD-10 coded data available.
 Mine hospital data
 South African medical scheme data - obtained from three different medical
scheme administrators.
All of these except one of the medical scheme sources were used during a previous
exercise to determine the costs of the minimum benefit package, and their strengths
and weaknesses are described elsewhere (Soderlund and Peprah, 1998). Results are
presented in the database as averages of all sources so that no data can be attributed to
any one source.
Two RVUs are provided:

An ‘overall costs’ RVU which includes hospital ward, drug, theatre, personnel
(including doctors), consumables and diagnostics costs.
 A hospital only RVU, which includes only those costs which would be
reimbursed to the hospital itself (i.e. ward, theatre, in hospital drugs and
consumables).
The data to calculate the ‘in-hospital’ RVU were available only from a subset of the
South African Medical Schemes data, and the sample size used was thus much
smaller than for the ‘overall cost’ RVU. Many of the ‘hospital only’ RVUs were
calculated from sample sizes of less than 10 cases, and estimates are thus unreliable.
There are also a greater number of categories for which no RVU could be calculated
at all. We would thus encourage the use of overall RVUs. RVUs were calculated
for each data source such that the average for a South African Medical Scheme
population equalled 1. An unweighted average of the sources was then taken for each
MBC. Where only one data source contributed data for an MBC, then this provided
the sole RVU estimate. Data were then re-standardised to the medical scheme
population. Both the hospital and the overall RVUs have a weighted average of 1.
3.2 Resource predictive power
3.2.1 Data sources
A single South African medical schemes claims dataset, consisting of approximately
25000 hospital admissions, was used to assess the resource-predictive power of
MBCs.
3.2.2 Statistical methods
Casemix systems are designed to group together treatment episodes that would be
expected to consume similar amounts of resources (or, by proxy, involve similar
lengths of hospital stay). The two dependant variables under study are thus length of
stay and cost. The extent to which variation in either of these variables occurs
between casemix groups, rather than within them, thus determines the strength of the
grouping system. Mathematically, this can be expressed as a ratio:
SSBG = R2 , or the Reduction in Variance (RIV) due to the grouping
SST
Where:
SSBG = Between groups sum of squares, or model sum of squares.
SST = Total sum of squares
Obviously, if n episodes are divided into n different groups, all variance will be
between group, and none within group, yielding an RIV statistic of 1. It follows that
the RIV statistic cannot decrease with increasing splitting of existing groups.
Similarly, the chance of a random binary split reducing the variance in LOS in a
sample of 10 episodes is much greater than in a sample of one hundred episodes. It
thus follows that an appropriate statistic for comparison should also take into account
parsimony (i.e. the number of groups) and sample size. Two commonly used statistics
that reflect this preference for more parsimonious models are the F-statistic and the
mean squared error (MSE):
MSE = SSWG / (n-r)
and,
F =
MSBG
MSE
=
SSBG / (r-1)__
SSWG / (n-r)
where:
SSWG = Sum of squares within groups, or error sum of squares
r = number of groups
n = number of patient episodes
Both can be considered an index of grouping efficiency given equal sample sizes. An
increase in F and a decrease in MSE are associated with better model fit. RIV, MSE
and F-statistics are generated by standard Analysis of Variance (ANOVA) or
regression routines available on most statistical software packages.
A relatively small number of episodes treated may have extraordinarily long lengths
of stay, and / or high costs. Most of these are not typical episodes of acute care, and
there are generally non-diagnostic factors, such as family circumstances, underlying
such episodes. It is difficult to distinguish such episodes on diagnostic or
demographic identifiers alone, and they can only really be eliminated by excluding all
cases staying longer than an arbitrary cut-off point, or costing more than a certain
threshold. None of the proprietary casemix measures have been designed with the aim
of predicting very long hospital stays, so appropriate trimming is essential in
comparing them. Comparisons have been conducted using data after excluding
episodes longer than 29 days. This trimming technique was chosen to make the results
comparable with those conducted elsewhere (Soderlund et al. 1996). The aim of
trimming is to remove atypical episodes from the data, and thus judge the grouping
method on ‘reasonable’ data.
Unfortunately, we could not compare the performance of MBCs in explaining
resource use with standard casemix measurement systems because none of the
routinely available systems can read procedure codes in common use in South Africa
(CPT-4 or Gazette codes). The RIV statistic is comparable across different datasets
and settings, however. In general, on acute hospital data trimmed at 29 days we
would expect an RIV statistic for costs or length of stay in excess of 25% for a
reasonable resource-classification system. Comparison data were drawn from
published evidence of grouping performance of the UK HRG categorisation system
(Soderlund et al. 1996)
3.2.3 Results
RIV performance for MBCs is presented in Table 1 below.
Table 1. RIV performance of MBCs
MBCs on SA data
Costs
17%*
LOS
23%
* total amount claimed, rather than costs
UK HRG v2
grouper on UK data
25%
31%
On average, MBCs are approximately 35% worse than HRGs in explaining resource
use in each of their respective settings.
While the performance figures are surprisingly good given that MBCs were never
intended as a resource categorisation system, they are significantly worse than would
be required for a valid reimbursement system. The statistics are supported by a
cursory examination of the categories. For example, for a leukaemic admitted to
hospital for a day’s chemotherapy the hospital would be reimbursed the same amount
as for a 3 week long admission for a bone marrow transplant. The intention of using
such methods of reimbursement is to incentivise providers to allocate resources
according to need, based on an appropriate sharing of risks and responsibilities. It
also requires that genuine, unavoidable costs be adequately funded. In the case of
the leukaemic, reimbursing on the basis of MBC would simply result in hospitals not
offering bone marrow transplants.
3.2.4 Conclusions
While MBCs might superficially look somewhat like a DRG-type categorisation
system, both their face-validity and statistical performance suggest that they should
not be used for this purpose. The RVUs provided might be used for internal
benchmarking purposes and cost projections, but should probably not be used as the
basis for payment of providers. Migration to diagnosis-based global fees for
reimbursement purposes remains a desirable objective however. Two approaches are
suggested to achieve a suitable tool for this purpose:
1. Adaptation of MBCs to reflect resource homogeneity. By adding an extra digit,
or even a binary split to each MBC, their resource homogeneity could likely be
substantially improved. The positioning of these splits would require extensive
statistical analysis using well validated data, however, and is not an exercise
that should be taken lightly. The end result would be a home-grown ‘DRG
type’ categorisation system. An added problem with this approach is that the
Minimum Benefit categories are likely to be undergoing repeated change over
the coming years. This will imply a change in reimbursement values
irrespective of whether this is in line with resource homogeneity. The final step
in this process would be to hard-code the categorisation system into an
automated grouper, which would automatically assign both MBCs and resource
categories/RVUs.
2. Adaptation of an imported categorisation system to suit local needs. A
diagnostic categorisation system, such as the HCFA DRGs, could be adapted to
read local diagnosis and procedure codes. In this instance, the basic structure of
the categories would already be designed for resource homogeneity, and they
would only require adaptation where South African clinical practice differed
significantly from practice in the country of origin. This would appear to be a
more focused and realistic approach, rather than trying to patch up a
categorisation system that was designed for an entirely different purpose. It
would also be congruent with approaches used in most other countries.
4. References
Soderlund, N., Gray, A., Milne, R. and Raftery, J. (1996) Case mix measurement in
English hospitals - an evaluation of five methods for predicting resource use. Journal
of Health Services Research and Policy 1, 10-19.
Soderlund, N. and Peprah, E. (1998) An essential hospital package for South Africa selection criteria, costs and affordability - Monograph no 52, Johannesburg: Centre
for Health Policy.