DECISION SUPPORT SYSTEM FOR HOSPITAL QUALITY ASSESSMENT

• US 20170053080A1
• Filed: 04/29/2015
• Published: 02/23/2017
• Est. Priority Date: 04/30/2014
• Status: Abandoned Application

• Current Assignee: Battelle Memorial Institute
• Original Assignee: Battelle Memorial Institute
• Inventors: Geppert, Jeffrey J., Strauss, Warren, Morara, Michele
• Application Number:
  US15/307,821
• Publication Number:
  US 20170053080A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A decision support system comprises receiving a request from a client computer to derive a quality assessment associated with a health care provider of interest, receiving an identification of a user-selected benchmark, determining a comparison range over which data from the data source is to be analyzed, identifying a set of quality measures, generating a first data set of quality measure performance by evaluating the set of quality measures against underlying medical data in a data source filtered by the range, generating a second data set defining an estimated quality measure performance using a probabilistic forecasting model to evaluate the set of quality measures by drawing inferences about the set of quality measures beyond a period of time for which the underlying medical data is available. An overall quality indicator score is computed, based upon a comparison of the first data set and the second data set.

Claims

1. A machine-executable process for computing reference and benchmark data for evaluating healthcare providers comprises:

obtaining at least two data sets including a first data set, and a second data set, wherein;

the first data set includes present on admission data that represents a condition of a patient that is present at the time an order for inpatient admission occurs; and

the second data set does not require present on admission data;

establishing quality measures including obtaining a set of quality indicators;

evaluating each of the first data set and the second data set against the obtained quality indicators;

calibrating, by a processor, the expected present on admission data of the first data set as a Recalibration Factor such that an overall observed rate (P) equals an overall expected rate (E[P|X]) for each quality measure of the first data set;

using, by the processor, the Recalibration Factor to calculate expected present on admission data on the second data set;

using the calculated expected present on admission data of the second data set to calculate an observed and expected outcome of interest on the second data set;

using the calculated observed and expected outcome of interest of the second data set to forecast an observed and expected outcome of interest for the second data set;

using the calculated observed and expected outcome of interest on the second data set and the forecasted observed and expected outcome of interest on the second data set to calculate an overall observed-to-expected ratio and a reference population rate (K) for each quality measure of the second data set; and

using a predetermined signal variance and the reference population rate on the second data set to calculate a national benchmark for each quality measure.

2. The machine-executable process of claim 1 further comprising:

obtaining a third data set that includes present on admission data;

evaluating the third data set against the obtained quality indicators;

computing a preventability score that characterizes a proportion of adverse events that were potentially preventable in accessing an healthcare provider of interest, by;

obtaining reference and benchmark data;

using the calculated expected outcome of interest on the second data set and the forecasted expected outcome of interest on the second data set to calculate an expected outcome of interest on the third data set;

using an observed outcome of interest of the third data set, the calculated expected outcome of interest on the third data set, and the reference population rate from the second data set to calculate a risk-adjusted rate on the third data set and a noise variance on the third data set, for each quality measure in the third data set; and

using the risk-adjusted rate on the third data set, the noise variance on the third data set and a predetermined signal variance to calculate a performance score on the third data set and a “

posterior variance”

on the performance score on the third data set for each quality measure.

3. The machine-executable process of claim 2, wherein using the calculated expected outcome of interest on the second data set and the forecasted expected outcome of interest on the second data set to calculate an expected outcome of interest on the third data set, comprises calculating the expected outcome of interest (E[Y, P=0|X]) for each discharge and quality measure of the third data set.

4. The machine-executable process of claim 2 further comprising:

computing a noise variance on the third data set as;

variance (risk-adjusted rate on the third data set).

wherein using an observed outcome of interest on the third data set, a calculated expected outcome of interest on the third data set, and the reference population rate from the second data set to calculate a risk-adjusted rate on the third data set and a noise variance on the third data set, for each quality measure in the third data set comprises;

computing a risk-adjusted rate on the third data set as;

(observed rate on the third data set/expected rate on the third data set)*reference population rate on second data set.

5. The machine-executable process of claim 2 further comprising performing at least one of:

computing reliability-weight (W) as a (signal variance/(noise variance on the third data set+signal variance));

computing the performance score as a risk-adjusted rate on third data set*W+reference population rate on the second dataset*(1−

W); and

computing a posterior variance is computed as a signal variance*(1−

W).

6. The machine-executable process of claim 2 further comprising:

using the national benchmark, the performance score on the third data set, and a posterior variance on the performance score of the third data set to calculate a proportion preventable on the third data set for each quality measure.

7. The machine-executable process of claim 6 further comprising:

determining a posterior distribution by parameterizing a gamma distribution using the performance score and the square root of the posterior variance to calculate alpha and beta.

8. The machine-executable process of claim 6 further comprising:

using the proportion preventable on the third data set for each quality measure to calculate an overall preventability score (PS).

9. The machine-executable process of claim 8, wherein using the proportion preventable on the third data set for each quality measure to calculate the overall preventability score (PS) comprises:

calculating the overall preventability score as a weighted average of the proportion preventable across each quality measure, where the weight equals the number of predicted adverse events for each quality measure.

10. The machine-executable process of claim 9 further comprising:

determining predicted adverse events as a function of a performance score*number of discharges in the population at risk.

11. The machine-executable process of claim 2, wherein:

obtaining at least two data sets including a first data set, and a second data set comprises;

obtaining the first data set as at least one state-wide inpatient database (SID), where each SID is obtained from the Healthcare Cost and Utilization Project (HCUP); and

obtaining the second data set as a Nationwide Inpatient Sample (NIS) from the Healthcare Cost and Utilization Project (HCUP); and

obtaining a third data set comprises;

obtaining the third data set as a Hospital Association (HA) data set that includes data over a more recent time period than the first data set.

12. The machine-executable process of claim 2, wherein evaluating each of the first data set and the second data set against the obtained quality indicators, comprises:

processing, by the processor, the first data set by;

applying the quality indicators against the first data set to calculate an observed present on admission (P) value for each discharge and quality measure of the first data set; and

calculating an expected present on admission (E[P|X]) for each discharge and quality measure of the first data set;

processing, by the processor, the second data set by;

calculating an observed outcome of interest (Y) for each discharge and quality measure of the second data set;

calculating an expected outcome of interest (E[Y|X]) for each discharge and quality measure of the second data set; and

calculating an expected present on admission (E[P|X]) for each discharge and quality measure of the second data set;

evaluating the third data set against the obtained quality indicators comprises processing the third data set by;

calculating an observed outcome of interest (Y) for each discharge and quality measure of the third data set;

calculating an observed present on admission (P) value for each discharge and quality measure of the third data set; and

calculating an expected outcome of interest (E[Y|X]) for each discharge and quality measure of the third data set.

13. The machine-executable process of claim 1, wherein establishing quality measures comprises:

obtaining quality indicators that comprise at least one of;

Inpatient Quality Indicators (IQI), Patient Safety Indicators (PSI) and Pediatric Quality Indicators (PDI).

14. The machine-executable process of claim 1, wherein using the Recalibration Factor to calculate expected present on admission data on the second data set comprises:

calculating the expected present on admission (E[P|X]) for each discharge and quality measure of the second data set.

15. The machine-executable process of claim 1 further comprising performing at least one of:

using the calculated expected present on admission data of the second data set to calculate an observed and expected outcome of interest on the second data set by calculating the observed outcome of interest (Y, P=0) for each discharge and quality measure of the second data set;

using the calculated expected present on admission data of the second data set to calculate an observed and expected outcome of interest on the second data set by calculating the expected outcome of interest (E[Y, P=0|X]) for each discharge and quality measure of the second data set; and

using the calculated observed and expected outcome of interest of the second data set to forecast the observed and expected outcome of interest by forecasting the observed and expected outcome of interest using a linear trend of the observed-to-expected ratio for each healthcare provider with a periodic effect.

16. The machine-executable process of claim 1, wherein using a predetermined signal variance and the reference population rate on the second data set to calculate a national benchmark for each quality measure, comprises specifying the national benchmark as a percentile in a performance score distribution.

17. The machine-executable process of claim 1, wherein:

establishing quality measures including obtaining a set of quality indicators, comprises

obtaining the Agency for Healthcare Research and Quality (AHRQ) quality indicator (QI) software; and

evaluating each of the first data set and the second data set against the obtained quality indicators comprises using the obtained software to evaluate the quality indicators against the first data set and the second data set.

18. The machine-executable process of claim 1, wherein establishing quality measures including obtaining a set of quality indicators further comprises:

mapping data elements and data values from the first data and the second data set to an AHRQ QI Software data dictionary.

19-33. -33. (canceled)