Analysis of hospital readmission using SAS Visual Analytics (Data Explorer and Report Designer)

1. Re-admit Historical
By:
Monika Mishra

2. Introduction
A hospital readmission is an episode when a patient who had been discharged from a hospital is
admitted again within a specified time interval. Readmission rates have increasingly been used as
an outcome measure in health services research and as a quality benchmark for health systems.
Hospital readmission rates were formally included in reimbursement decisions for the Centers
for Medicare and Medicaid Services (CMS) as part of the Patient Protection and Affordable Care
Act (ACA) of 2010, which penalizes health systems with higher than expected readmission rates
through the Hospital Readmission Reduction Program. While many time frames have been used
historically, the most common time frame is within 30 days of discharge, and this is what CMS
uses.
About the dataset
 This is a dataset provided by the SAS.
 The dataset provides details about the hospital readmission for 10 states with around 163
cities.
 The dates being considered is from June 2011 to July 2012.
Dataset details
Number of columns 51
Number of rows 142,002

3. Goals
The study aims to provide a statistical analysis of the readmission history of the ten states in US
between June 2011 and July 2012.
The study will help in understanding the general trend of operation count over the years. A co-
relationship between different variables such as Admit length of stay, Readmit length of stay,
number of chronic conditions and number of visits will be studied.
The All Patient Refined DRGs (APR-DRG) incorporate severity of illness subclasses into the
AP-DRGs. The APR-DRGs expand the basic DRG structure by adding four subclasses to each
DRG. The addition of the four subclasses addresses patient differences relating to severity of
illness and risk of mortality. The four severity of illness subclasses and the four risk of mortality
subclasses are numbered sequentially from 1 to 4 indicating respectively, minor, moderate,
major, or extreme severity of illness or risk of mortality. Our study will help in identifying the
number of visits by APR-DRG severity. It will also help in comparing the severity in males and
females.
The study will help us in identifying the root cause of the admission/readmission to the hospital.
It will also give us some idea about the state accounting for maximum number of visits and
charges associated with it. Which department had maximum operation count and how much
order charge it accounts for – all of these questions will be answered in this study.
Based on the facts provided by the study, a measure can be taken to restrict the future hospital
readmission.

4. Data Explorer
1. What is the trend ofoperation count by admit date?
Chart used:
 Time Series Forecasting
Analysis:
The above visualization shows the sum of operation count by admit date. The operation
count was at peak around February 2012 which was around 14000. The graph also shows the
forecast of the operation count. That seems to be decreasing over the coming months.

5. 2. What is the correlation between different measures ofthe Readmit_Historical data?
Chart used:
 Correlation Matrix
Analysis:
The above chart shows a correlation matrix of the Admit Length of Stay, Readmit Length of
Stay, Number of chronical conditions, number of visits and readmitted. The color gradient
Blue -Red is used to display the extent of relationship between different measures. The
readmit length of stay and admit length of stay shows a strong co-relationship. The
num_chronic_cond and readmitted shows a somewhat close relationship. Rest have a weak
relationship among themselves.

6. 3. What is the number ofvisits by the DRG_APR_SEVERITY? Howis it different in male
and female?
Chart used:
 Box Plot
Analysis:
The above visualization shows the number of by the drg_apr_severity. The field “Gender”
has been used in the lattice column to differentiate between males and females. Females have
highest number for visits for level 3 of dgr_apr_severity while for males, it’s 1. A reference
line at 16 on Y axis has also been used for better clarity.

7. 4. Which words are frequently used for the description ofthe diagnosis?
Chart used:
 Word Cloud
Analysis:
The above word cloud shows the frequency of the sentences used in the description of the
diagnosis. The larger the font of the sentences, greater is the frequency. The phrase
“Pneumonia organism unspecified” has the highest frequency. It can also be inferred that the
maximum diagnosis phrase is related to the heart.

8. 5. What is the number ofvisits and order total charges state wise?
Chart used:
 Tree Map
Analysis:
The above tree map shows the number of visits and order total charges by state. It can be
clearly seen that the maximum number of visits as well as order total charges is the greatest
for Florida state. The size of the tree map section is based on number of visits and the color is
based on the order total charges. The feature “hierarchy” is used to club “department” under
“statecode”. One can double click the statecode to drill down to the department for further
analysis.

9. Report Designer
1. What is the number ofchronic conditions and number ofvisits state wise?
Chart Used Dual Axis Bar Chart
Report Control Used Drop-Down List
Analysis:
The above chart displays the number of chronic conditions and number of visits state wise. It
can be seen that the larger the number of chronic conditions, larger is the number of visits to
the hospital. Florida is leading in both the measures. A drop-down list is also used to filter it
by the diagnosis group.

10. 2. Which department had maximum operation count and what is the order total charge ?
Chart Used Dual Axis Bar-Line Chart
Report Control Used Button Bar
Analysis:
The above dual axis bar-line chart shows the statistics of operation count and order total
charges department wise. The maximum operation count is for the heart department followed
by general medicine. The order total charges seem to be directly proportional to the operation
count. The button bar feature is used to see the male and female statistics from the above
chart.

11. Summary/ Story Telling
Discharging patients from the hospital is a complex process that is fraught with challenges and
involves over 35 million hospital discharges annually in the United States. The cost of unplanned
readmissions is 15 to 20 billion dollars annually. Preventing avoidable readmissions has the
potential to profoundly improve both the quality of life for patients and the financial wellbeing of
health care systems.
Readmissions are costly, often doubling the cost of care for one of these episodes and that is why
it is a key performance indicator. But readmissions have multiple causes including,
Discharge too early before the patient is adequately stable.
Discharge to a location, e.g., home, visiting nurse, skilled nursing facility or nursing home that
cannot support recovery.
Recurrence or worsening of the original disease because of poor patient compliance, inadequate
supervision or follow-up, or just bad luck (Dinerstein, 2018).
Several factors that increase the likelihood of readmission may be modifiable, especially those
that relate to clinician or system level issues. Such factors include:
 Premature discharge
 Inadequate post-discharge support
 Insufficient follow-up
 Therapeutic errors
 Adverse drug events and other medication related issues

12.  Failed handoffs
 Complications following procedures
 Nosocomial infections, pressure ulcers, and patient falls
As healthcare in the United States shifts toward a more value-based model, reducing readmission
rates has become one of the biggest challenges healthcare organizations now face. Last year,
approximately half of all hospitals in the country collectively lost more than $500 million in
reimbursements because they had not learned to overcome this roadblock.
Reducing preventable hospital readmissions is a national priority for payers, providers, and
policymakers seeking to improve health care and lower costs. In 2012, the Centers for Medicare
& Medicaid Services began reducing Medicare payments for certain hospitals with excess 30-
day readmissions for patients with several conditions:
Heart attack
Heart failure
Pneumonia
Chronic obstructive pulmonary disease
Hip or knee replacement
Coronary artery bypass graft surgery

13. Source: MedPAC analysis of 2008 through 2016 Medicare claims files for Medicare FFS beneficiaries age 65 or older.
In my visualization too, the heart department had the maximum operation count.
The following factors as critical to reducing avoidable 30-day readmissions:
 Establishing care goals for patients with serious illnesses and involving the patient in the goal
setting.
 Making sure patients have a clear understanding of who to contact after discharge should
problems arise.
 Coordinating follow up appointments with both patients and primary care doctors, and ensuring
patients have a way to get to appointments.
 Improving communication between hospital staff and health care professionals serving the patient
outside of the hospital.
Educating your hospital inpatients before their transition home is an important part of helping
them understand care goals. The Patient Channel and The HeartCare Channel offer engaging

14. programming on heart failure, chronic obstructive pulmonary disease (COPD), knee and hip
replacement, and heart attack post-discharge care. These can be powerful tools in your efforts to
reduce 30-day readmissions rates and consequent Medicare payment penalties (Martin, 2016).
In an effort to help patients avoid unnecessary and avoidable hospital readmission after an
illness, a program known as the Hospital Readmission Reduction Program, or HRRP for short,
was created. This program, which was established in 2012 as part of the Affordable Care Act,
penalizes hospitals financially if readmission rates for certain illnesses are higher than expected
(Walker, 2017).
With hospital and federal dollars going to hospital readmissions, CMS created a value-based
reimbursement program that penalizes hospitals for excessive readmission rates for six
conditions, including chronic lung disease, heart attacks, and hip and knee replacements. The
Hospital Readmissions Reduction Program (HRRP) decreased rates by 8 percent nationally
between 2010 and 2015 (LaPonte, 2018).
The Hospital Readmissions Reduction Program is a well-intentioned program crafted to make
hospitals more accountable for the care patients receive after discharge. The evidence suggests
that its benefits have been small and its costs potentially large. By making changes to the
program, Congress and the Trump administration can signal that improving care for Medicare
beneficiaries is a serious priority. Heeding the evidence would be a good place to start (Jha,
2017).

15. References
Dinerstein, C. (2018, October 03). The Continuing Problem Of Hospital Readmissions.
Retrieved from https://www.acsh.org/news/2018/10/03/continuing-problem-hospital-
readmissions-13467
Jha, A. K. (2017, December 20). JAMA Forum: To Fix the Hospital Readmissions Program,
Prioritize What Matters. Retrieved from https://newsatjama.jama.com/2017/12/20/jama-
forum-to-fix-the-hospital-readmissions-program-prioritize-what-matters/
LaPointe, J. (2018, January 8). 3 Strategies to Reduce Hospital Readmission Rates, Costs.
Retrieved from https://revcycleintelligence.com/news/3-strategies-to-reduce-hospital-
readmission-rates-costs
Martin, P. F. (2016, June 16). New Study Finds 27% of 30-Day Hospital Readmissions Are
Preventable. Retrieved from https://www.thewellnessnetwork.net/health-news-and-
insights/blog/new-study-finds-27-30-day-hospital-readmissions-preventable/
Walker, B. (2017, December 20). Hospital Readmission Statistics You Need to Know. Retrieved
from https://insights.patientbond.com/blog/hospital-readmission-statistics-you-need-to-know