Unleashing Allied Health Data


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Some ideas on how we should be using allied health activity better.

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  • There are already a number of IT systems that are probably already in use at many of your hospitals and it is important to appreciate that allied health activity data may already be stored within some of the financial systems already in use. It is important to link allied health activity to patient episode of care so that cost savings, can be highlighted to others.
  • Is this what is going to be used by DOHA to facilitate activiy based funding?
  • David Fetter and his group developed DRG’s to help provide a standard way to quantify the outputs of hospital. One of the problems with this approach is that it encompasses everything, allied health provide about 15-20% of all the care patients receive in hospital and it is important that we capture our value.
  • Defining DRG’sFetter, et al., (1980) originally used four key underlying principles to define casemix groups :the class or group definitions are based on hospital abstracts i.e. the information routinely collected by hospitals in discharge summaries (e.g. sex, age, principal diagnosis, secondary diagnosis, surgical procedures performed etc) a practical number of classes for the purpose and context of classification (to many would make things to complicated)There are similar patterns of resource intensity use within each class. i.e the average pattern of resource use within each group can be predicted. In order to define the various case mix groups identify the ordinary, the usual and the routine, and then applying the techniques of statistical process control, to filter out and examine the aberrant cases to understand the causes of the aberrations make use of data from hospital discharges to determine (Fetter, 1999)there are similar types of patients in a given class from a clinical perspective.(Fetter, et al., 1980)
  • This diagram shows how DRG’s are allocated
  • This system has been refined to capture some of the complexities inherent in some of the patient groups
  • This is an example highlighting the contacts a patient has before being discharged. A considerable amount of these contacts are from allied health and by having a common denominator/minimum data set we will be able to save time by collectively gathering the information from patients during their admission to help provide detailed discharge summaries for continuing care is an easy win.
  • For my masters thesis I took all the allied health cost centres within the finance system and paired these costs to each patients episode number, and then the episode number to the DRG separations for one year at our hospital. This gave me an indication of the top ten DRG groups by allied health cost input. From here I went on to look at the numbers of patient seen by allied health for each DRG group and then looked at the number of inliers and outliers. My original hypothesis was that allied health cost would form a significant part of total hospital reimbursement and as it is not captured within the DRG cost and that those patients seen by allied health were less likely to be outliersUnfortunately although I had every intention of making use of activity based costing to determine allied health cost per minute intervention, the data quality was extremely poor. The cost of allied health intervention is not accurately captured within the health system and may be one of the reasons why we are not considered when large funding decisions are made..
  • Because I was unable to accurately contact the allied health activity cost I went on to try see if I could find examples of interdisciplinary practice. The idea being that if within a certain hospital patients of a certain DRG group were more likely seen first by a certain staff group, they in turn could make a referral onto the other allied health groups required so that they could be seen sooner and hopefully discharged without becoming outliers or overstays.The above is basically a venn diagram , size of the circle is in proportion to the size of the group as is the amount of overlap. I was only able to do these diagrams for three staff groups and I tried to look at the median LOS for the patients in each area within the venn diagram, again to see if there were examples of synergy between the proffessions.This is when the idea of looking at seeing if we could look at using details of allied health intervention as a means of helping to define DRG groups came to me. DRG’s are only coded when patients are discharged home, as allied health capture activity weekly we are in a situation where we could identify patients before they go home. If there are a few DRG groups within your hospital that have a high number of outliers that incur the hospital losses allied health may be able to help. If you were to look at the pattern of activity for allied health staff for this group, via statistical techniques like machine learning you could set up some paramaters to identify patients so that intervantions could be increased to reduce the risk of overstay.Some of these statistical methods are form the field of data mining
  • New techniques were brought into the field from related areas, such as machine learning; the realization that modern data analysis draws up from a whole range of related disciplines has give rise to the establishment of the broader field of data mining
  • Following on from some of the great work that has been done in South Australia, Queensland and Sydney that was presented at the meeting in terms of defining a minimum data set to help inform activity based costing and help improve quality.
  • The data model in use by the health round table relies on participating sites to record their allied health inpatient and outpatient clinical time or activity. There
  • There is along way to go before we can fully go paperless, however by building the blocks now we will be in a better place later.
  • The issues of interoperability need to be really addressed as the ability to communicate with providers is an integral part of any system. Also by being compliant there is the potential for software to be replaced with other compliant software later on.
  • The nutrition care process, although specific to dietetics could be applied to other allied health groups
  • Unleashing Allied Health Data

    1. 1. Nathan Billing
    2. 2.  Where does data reside in Hospitals Casemix What are DRG’s Interdisciplinary practice Data mining Potential analysis techniques Round table Limitations Going paperless
    3. 3. 3
    4. 4. “Casemix is not a health policy in its own right.It is a benchmark pricing system designed toensure that the same price is paid for the samework by like hospitals - no matter where it isundertaken (within planning parameters). Itemphasises technical (cost) efficiency”Chris Brook
    5. 5.  based on hospital abstracts a practical number of classes for the purposeand context of classification There are similar patterns of resourceintensity use within each class. i.e theaverage pattern of resource use within eachgroup can be predicted there are similar types of patients in a givenclass from a clinical perspective.(Fetter, et al., 1980)
    6. 6.  The results obtained by data mining, inparticular from the subfield of machinelearning, may not only be exploited toimprove the quality of care by implementingparticular changes to care policies but canalso be used as a basis for the construction ofcomputer-based decision support
    7. 7.  Accuracy of information captured Between site variability is not (to myknowledge) accounted for in activity data i.e.: Staffing level variation during data capture time Geographical and demographic variation s Funding issues and organizational prioroties Experience of allied health staff Number of staff ?? Number of refferals
    8. 8. http://care360blog.questdiagnostics.com/transitioning-to-ehr/infographic-ehr-vs-traditional-paper-records/
    9. 9. http://wiki.hl7.org/index.php?title=Nutrition_Management