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 BAKristlaug H. Jónasdóttir BS, MSc

Landspítali University Hospital (LSH)Key statistics 2008

Population in Iceland 319.326

Number of individuals receiving hospital care 106.699

Outpatient units - visits 367.540Day units - visits 93.422Emergency department - visits 94.650Hospital at home service - visits 14.798

Admissions 28.607Patient days 232.570Average length of stay (LOS) 8,1

Average LOS Excluding Division of Rehabilitation and Division of Geriatrics 5,2Patient acuity 1,18

Deliveries 3.376Surgical procedures 14.583Diagnostic imaging 123.956

Number of employees (at the end of the year) 5.022Full-time equivalents (mean) 3.872

Outliers as persetages of total admissions 3.0%Outliers cost as a percentage of total operational cost 21,5%

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.

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.

Hypothesis

p measures the probability that a patient will become an outlier.

T0 :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.

Log (p/ 1-p) =c+β1*gender+ β2*age (+70) +β3*age (0-18)+β4 * ln(Number of IDC-10 diagnosis)+β5* ln( Number of NCPS+ theraphutic procedures)+β6* Types of admissions+β7* Types of discharges_MORS+β8*Types of Discharges_Other+β9*Ln(LOS)+e

Calculation of outliers

Outliers are admissions that exceed a certain cost limits calculated within each DRG group, see formula below.

Outlieri = Q3i + k *(Q3i – Q1i)

k = (P95 – 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 95th percentile.

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)

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)

Methodology: Sample

Number percent Number of outliersTotal number 21.912 / 703 3%Gender Male 9.194 42% 322 3,5%

Female 12.718 58% 381 3,0%

Types of adamission Acute 17.494 80% 598 3,4%Non acute 4.418 20% 105 2,4%

Discharge Home 19.895 91% 406 2%Mors 341 2% 49 14%Other* 1.676 8% 248 15%*Nursing-homes, other hospitals and other institutes

Sample

Methodology

Logistic regression

predict the probability of Y occorrung given known values of predicting variables

Result

Acute admission* 1,77 0,01Length of stay* 1,94 0,01Number of ICD-10* -0,36 0,01Number of NCSP* 1,20 0,01Mors* 1,32 0,01Transferd* 0,46 0,0117 years and yonger* 0,78 0,0170 years and older** -0,30 0,001Constant -8,41 0,01

Change in risk

p<

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.

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.

Use of the result

The purpose is not to decrease outliersThe 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.

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.

Result I