1. Recommendations on Minimum Data Recording Requirements in Hospitals from the Directorate of Health in Iceland: Is it possible to use Hospital Patient Registry data to decrease the cost of outliers Arnar Berþórsson BA Kristlaug H. Jónasdóttir BS, MSc

2. Landspítali University Hospital (LSH)

3. Prospective Payment Systems (PPS) and Diagnosis Related Groups (DRG)  Fixed payment per discharge.  Payment is the same for all patients within each DRG group.  Patients within each DRG group should show homogeneity in clinical conditions as well as in cost.  Payment for DRG groups is based on average costs for patient within the group.  Patients grouped based on:  Principle diagnosis ICD-10  Secondary diagnosis ICD-10  Procedures and imaging examination NCSP+  Length of stay  Age  Gender  Type of discharge  DRG weight: mean cost in each DRG divided by total mean cost in all DRGs.

4. Outliers  An observation that is numerically distant from the rest of the data.  In most large samples of data, some data points will be further away from the sample mean than what is deemed reasonable  They can occur by chance, but they can also be an indicator of either measurement- or coding errors or that the data has a heavy-tailed distribution.  In health care reimbursement, especially in PPS, outliers are those patients that require an unusually long hospital stay or whose stay generates unusually high costs.

5. Hypothesis p measures the probability that a patient will become an outlier. T 0 :Following model, based on Guidelines from the Directorate of Health for minimal registration requirements for patient information, can be used as an indicator for a patient’s probability of becoming an outlier.

6. Calculation of outliers  Outliers are admissions that exceed a certain cost limits calculated within each DRG group, see formula below. Outlier i = Q3 i + k *(Q3 i – Q1 i ) k = (P 95 – Q3) / (Q3 – Q1) Where Q1 is 25th percentile, Q3 is 75th percentile and k is a constant that set the outlier limit to 5 percent. P95 is 95 th percentile.

7. Methodology  Research design: Non-experimental analytic analysis.  Sample: Discharges from all wards within LSH except:  Long term Geriatric wards  Long term Psychiatric wards  Rehabilitation wards  Palliative care ward  Healthy newborns  Sample criteria:  Discharges in the period 1. Jan – 31. Des 2008 ( n=21.912 )  Cases classified into DRG groups  DRG groups ≥ 30 cases ( 196 DRG groups )  Data analysis: Logistic regression (stepwise method)

8. Methodology  Dependent variable: Outlier=1, Non Outlier=0  Independent variables :  Gender, 1=male, 2=female  Age, children ≤ 18, adults 19 to 69, elderly ≥ 70  Number of ICD-10, (International Classification of Deceases) codes, (Transformed to ln(x) to correct skewness)  Number of NCSP+ codes, (Nordic Classification of Surgical Procedures), (Transformed to ln(x) to correct skewness)  Types of admissions, acute =1, non acute =0  Types of discharges, home=1, died=2, other=3  Length of stay, (LOS) (Transformed to ln(x) to correct skewness)

9. Methodology: Sample

10. Sample

11. Methodology Logistic regression predict the probability of Y occorrung given known values of predicting variables

12. Result

13. Discussions  Why is it that with increasing number of registered diagnosis the probability of a patient becoming an outlier decreases??  Children (0-17) are more likely to become outliers than 18-69 years old  But older patients (70+) are less likely to become a outlier than 18-69 years old.  Death, mortality and length of stay provide strong evidence of who become an outliers.  Patient that are discharged to nursing homes, other hospitals and institutes are more likely to become an outlier.

14. Limitation  DRG groups with fewer than 30 discharges were ignored.  Cost is partly distributed by Length of stay, does this cause problem for the assumption to the model?  We could not use Marital Status  Distinguish between Discharges to other specialitis and to other institutions.

15. Use of the result The purpose is not to decrease outliers The purpose is to influence the factors that cause the patient to be a outlier. According to this study, outliers are 7 times more expensive than average patient in the same DRG group.

16. Further studies and ideas  Effect of marital status and discharge mode  Connection between number of registered diagnosis and outliers within DRG group  Add other relevant variables to the model such as Acuity, re- admission, waiting list, chronic diseases, test results….  Limit the sample to smaller groups such as single DRG groups or MDC groups or speciality.  Effect of quality of coding and homogeneity of DRG groups.

17. Result I