E inputoutcome studies a common output measure also

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(i.e. input/outcome) studies. A common output measure also facilitates meaningful comparisons of cost, quality and access. Casemix highlights differences (i.e. outliers). Analysis of such differences can lead to more streamlined and cost- effective clinical practice. One of the key lessons from the publication of hospital bill sizes on the Ministry of Health’s website was that hospitals with the lowest cost per unit of service (e.g. laboratory test, ward charge, etc.) need not necessarily have the lowest cost for a particular admission if these services are used inappropriately. For example, a hospital with the cheapest non-standard drug would still be more expensive than another hospital that uses generic drugs. A common output measure enables identification of practice variations. Review of cost data across public hospitals through utilisation management activities have shown several areas where practices could be further standardised. These included the use of investigations (for example, using “specific” individual tests instead of the more expensive “all-inclusive” panel tests), 3 and the use of expensive drugs (through prescribing guidelines and a system of internal checks to ensure compliance with guidelines). 4 It should, however, be emphasised that casemix does not tell us what the proper practice is. In fact, the outlier may be the hospital or department that is practicing optimally in terms of quality, cost and access. Limitation of Casemix A key feature for casemix classification systems is optimal number of groups (DRGs), i.e. not too many (which will cause some groups to have too few observations to allow conclusions to be drawn), nor too few (as overly large number of cases placed in the same class may conceal real differences between the cases). However, this feature is also the key limitation of casemix, i.e. its effectiveness at lower levels of dis-aggregation. DRGs work on the principle of “Law of Large Numbers”, i.e. with sufficient number of cases, the distribution of cases would assume a normal distribution. Thus while DRGs work well at higher levels of aggregation (e.g. at hospital or cluster level), they are less effective at lower levels of dis-aggregation (e.g. departmental or even individual doctor level) as the smaller number of cases at this level means that one or more outliers can potentially skew the averages significantly.
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  • Fall '18
  • CASEMIX

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