Objective: Hospital deferments for patients squared to post-acute care (PAC) are lengthier and expensive than routine discharges. Patient’s medical insurance coverage plays a desperate role in determining their PAC discharge disposition. At Unity Point Health, retrieving a patient’s insurance coverage information accedes the PAC discharge disposition process by four days. In this study, we implement predictive analytics for early prediction of PAC discharge disposition to foster the needed care, in the suitable location, just in time. Methodology: To study the existing PAC discharge disposition method at Unity Point Health we conducted a group discussion involving twenty-five patient care facilitators (PCF) and two registered nurses (RN). We manually retrieved sixteen hundred patient’s data (July 2018 through August 2018) from discharge notes and initial nursing assessment to conduct a retrospective analysis of PAC discharge disposition. The analysis is limited to the patients discharged to AR, or SNF. We employed predictive analytics to develop a clinical decision support system (CDSS) that can efficiently identify patients eligible for AR and SNF by the first day of their inpatient stay. All evaluations were conducted using the SPSS Modeler, RStudio, Microsoft Visio and Excel. Results: Chi-Squared Automatic Interaction Detector (CHAID) algorithm was selected to be the best fit model with an (a) overall accuracy of 84.16%, and (b) the area under the receiver operating characteristic (ROC) curve of 0.81. Conclusion: CHAID algorithm is recommended to develop CDSS that can steer early prediction of PAC discharge disposition and thus minimize inpatient length of stay. Early prediction of PAC discharge disposition enabled UnityPoint-Health in reducing inpatient length of stay by forty-four percent and recuperated patient flow.

1. EARLY PREDICTION OF POST-ACUTE CARE DISCHARGE
DISPOSITION USING PREDICTIVE ANALYTICS
-- An Opportunity to Minimize Inpatient Length of Stay

2. MOTIVATION
• Hospital deferments for patients squared to post- acute care (PAC) are lengthier and
expensive than routine discharges [1] [2].
• The underlying reasons for the extended stays and increased costs are influenced by
complex medical conditions [6] [7] [8], and delayed PAC facility placement.
• The Institute for Healthcare Improvement’s white paper suggests advance
planning, coordination and development of partnership with AR and SNF to
convalesce patient flow [9]. However, no significant research has been done to
address advance PAC discharge planning.

3. MOTIVATION CONTINUED
• Patient's medical insurance coverage plays a desperate role in determining their
PAC discharge disposition.
• At UnityPoint Health, retrieving a patient's insurance coverage information accedes
the PAC discharge disposition process and inpatient length of stay by four days.

4. CONTRIBUTION
• In this study, we focus on developing a prediction model using machine learning
algorithm that can be deployed as a CDSS for advance PAC discharge disposition
planning as suggested by the Institute for Healthcare Improvement. Advance PAC
discharge disposition planning will identify patients eligible for post-acute services
(AR or SNF) based on their initial nursing assessment.
• At present, 6-Clicks is the only effective and user-friendly tool developed by the
Cleveland Clinic Health System [13] that helps in PAC discharge disposition planning
[14]. However, 6-Clicks does not focus on advance PAC discharge planning and
fails to effectively classify patients requiring AR and SNF.
• At UnityPoint Health, 6-Click’s classification accuracy was found to be twenty
percent.

5. METHODOLOGY
• Group discussion / Survey and problem identification, (25 PACFs and 2 RN)
• Data collection, (Manual chart abstraction)
• Data preprocessing, and
• Model selection and measuring its impact.

6. DATA VISUALIZATION
• Age
• Gender
• Braden Scale Score
• Hester Davis Fall Risk
• Discharge Type

7. FINDINGS – TRADITIONAL METHOD
The department lacks any defined process map
The figure was developed based on information
gathered from the group discussion and might
not include all detailed steps involved in actual
practice.

9. FINDINGS – AGE
DISTRIBUTION
• Distribution of Age against
Discharge type. The blue histogram
illustrates the age of patients
discharged to AR and the red
histogram shows the age of patients
discharged to SNF. It can be
observed that elder people were
more likely to be discharged to SNF.
This is due to the likelihood of
younger patient’s ability tolerate the
three hours of therapy

10. FINDINGS- AGE
VS GENDER
• This is a pictorial representation of
age versus gender. The blue
histogram shows the age of the
female patients and the red
histogram represents the age
distribution of male patients.

11. FINDINGS-AGE
VS BRADEN
SCALE
• This represents the relationship
between patient's age and their
Braden scale score. The blue surface
shows the Braden scale score for AR
patients and the red surface shows
the same for SNF patients. The
patients discharged to SNF has
higher Braden Scale score than that
of AR patients.

12. FINDINGS-AGE
VS HESTER-
DAVIS RISK
• This shows the relationship between
Hester-Davis fall risk and patient's
age. The blue surface shows the
Hester-Davis fall risk for AR patients
and the red surface depicts the
same for SNF patients. SNF patients
have higher Hester-Davis fall risk
than that of AR patients

13. COMPARATIVE
ANALYSIS
Sl.
Number
Model Overall
accuracy (%)
Area under
ROC curve
1 LDA 83.33 0.79
2 CHAID 84.16 0.81
3 RT 72.50 0.68
4 LSVM 76.66 0.70
5 CART 80.00 0.51

14. CHI-SQUARED AUTOMATIC
INTERACTION DETECTOR ALGORITHM
• CHAID tree. It takes:
“Hester-Davis fall risk score”,
“Hypertension”,
“Scalp laceration”,
“Abnormality of gait”,
“Osteoarthritis”, and
“none” (no neurological condition)
as the top six significant input variables with the p-value less
than 0.05

15. IMPACT OF CHAID
PAC discharge method PAC service (average time)
Medical insurance confirmation
(average time)
Total time (average)
Traditional method 5 days 4 days 9 days
After implementing CHAID
5days
(PAC service: day 1 through day 5)
Medical insurance confirmation (day 1 through day 4)
0 days 5 days
Total reduction in time (average) 4 days (44.44%)

16. IMPACT OF CHAID CONTINUED
• The CHAID model is able to identify eligible AR and NSF patients during the initial
nursing assessment phase and thus enables the hospital to initiate communication
with the insurance company from the very first day of Inpatient stay which initial
was left for the end. Thus PT/OT evaluations, continued nursing assessments, and
all other essential clinical activities can be processed meanwhile the medical
insurance company confirms the patient’s insurance coverage. Thus, the patient will
not have to wait extra four days to get insurance confirmation from the insurance
provider after the doctor recommends the discharge location. Moreover, this
model does not interfere with any medical and clinical process rather, encourages
and enables advanced PAC discharge planning by parallelly and proactively
gauging medical insurance coverage.

17. ADVANCED PAC
DISCHARGE
• New process map showing the
advantage of implementing CHAID
model. CHAID model removes the
extra four days of waiting time and
improves patient flow.

19. CONCLUSION
• CHAID is the best fit model with an overall accuracy of 84.16%. This model at Unity
Point Health can reduces the PAC discharge disposition and inpatient length of
stay by forty-four percent and also encourages advanced PAC discharge planning
as suggested by the Institute for Healthcare Improvement’s white paper.
• This approach is a good fit as it does not interfere with any clinical or medical
procedures and it easy to deploy as a CDSS. Moreover, the proposed CHAID model
outperforms 6-Clicks classification accuracy and is free of user bias. The results
obtained using CHAID is repeatable and reproducible. Additionally, application of
prediction modeling in PAC discharge has not been well established in any
published literature.

20. THANK YOU
Ms. Vanessa Calderon
Mrs. Shelby Neel