HCA 499 Module 3 Analyzing the Problem With Data Example

What the Data Show: Analyzing Heart Failure Readmissions at a Community Hospital

Student Name

Health Care Administration Program, Aspen University

HCA 499: Senior Capstone

Instructor Name

Month Day, Year

What the Data Show: Analyzing Heart Failure Readmissions at a Community Hospital

Before designing a solution, a capstone must understand the problem in detail. This paper reports the analysis stage of Dana's capstone: a year of readmission data, observation of discharges and interviews with staff and patients at a composite community hospital, combined into a picture of why heart failure patients return within 30 days.

Data Sources

Dana obtained deidentified data on all 312 heart failure discharges from the two medical units over 12 months: discharge date and day of week, unit, age, readmission within 30 days, days to readmission, readmission diagnosis, whether a follow-up appointment was scheduled before discharge and whether an outpatient visit occurred before readmission. She observed 10 discharges and interviewed 8 staff and 6 recently readmitted patients.

Key Findings

The table summarizes the main quantitative findings.

Timing

Two-thirds of readmissions happened within 14 days, suggesting that the first two weeks after discharge are the critical window. Interventions that act after day 14 would miss most readmissions.

Follow-Up and Readmission

Patients with a follow-up appointment scheduled before discharge were readmitted at about half the rate of those without. The same pattern shows up nationally, where heart failure patients treated at hospitals that got more of them in to see a physician soon after discharge returned less often (Hernandez et al., 2010). It also echoes the finding that half of Medicare patients readmitted after medical stays had no physician visit in between (Jencks et al., 2009).

Caution About Causation

The comparison does not prove that scheduling follow-up prevents readmission. Patients who got appointments may have been healthier, more organized or better connected to care. Dana noted this limitation and treated the finding as a strong lead to test, not a conclusion.

Readmission Reasons

Fluid overload caused 41% of readmissions, followed by other cardiac causes, infections and kidney problems. Fluid overload is often preventable with medication adjustment, daily weights and early response to weight gain, which pointed toward education and early contact after discharge.

Process Map

Mapping the discharge process revealed several gaps. Follow-up appointments were requested by nurses through a message to the cardiology clinic, which scheduled them days later by phone; many patients were never reached. Medication lists were reviewed quickly on busy afternoons. Education about daily weights depended on which nurse was on shift. No one called patients after discharge.

What Staff Said

Nurses said discharges often happen late in the day, leaving little time for teaching. Case managers said they lacked a way to book clinic appointments directly. A clinic scheduler said heart failure follow-ups competed with routine visits for limited slots. A hospitalist said patients often left before new medication instructions were fully explained.

What Patients Said

Readmitted patients described confusion about which medicines to take, not knowing whom to call when their weight rose and waiting for a clinic call that never came. One said, I thought someone would call me. These accounts matched the process gaps.

Fishbone Analysis

Dana organized causes in a fishbone diagram under people, process, technology, patients and policy. Process causes dominated: no reliable appointment scheduling before discharge, inconsistent education, no post-discharge contact. Technology causes included no direct scheduling access from the hospital record. Patient factors included limited health literacy and transportation barriers.

Pareto Ranking

Asking staff and patients to connect each readmission reviewed in detail to its main contributing factor produced a Pareto ranking: no scheduled follow-up, medication confusion and no early contact after discharge together accounted for about three-quarters of the contributing factors identified. Addressing these three would target most of the problem.

Implications for the Next Stage

The analysis points to an intervention bundle aimed at the first 14 days: scheduling follow-up before discharge, reconciling and explaining medications, and contacting patients soon after discharge. The literature review will test whether such components are supported by evidence; an earlier review had grouped exactly these activities, follow-up scheduling, medication reconciliation and post-discharge calls, among the common readmission interventions (Hansen et al., 2011).

Limitations

The analysis rests on a single hospital's records for a single year, readmissions to other hospitals may be missed, and interviews were few. The association between follow-up and readmission may be confounded. These limits will be acknowledged in the final report.

Data Quality

Dana checked data quality before analysis. Eleven records lacked a discharge disposition and were verified manually. Readmission diagnoses were coded by the quality department using standard definitions. She also confirmed that the same patient could not be counted twice for overlapping stays. Clean data made the findings more credible to clinicians who were initially skeptical.

Presenting the Findings

Dana presented the analysis to the sponsor and unit leaders with three charts: readmissions by days after discharge, readmission rates with and without scheduled follow-up, and the Pareto ranking of contributing factors. Keeping the presentation to three charts made the story clear. Nurses recognized the process gaps immediately, which built support for the next stages.

Stratifying the Data

Dana also broke readmissions down by age group, discharge day and unit. Rates were similar across age groups but higher for Friday discharges, 29% compared with 21% for other days, when fewer staff were available to arrange follow-up. That detail later shaped weekend and Friday planning for the intervention. Stratifying data often reveals patterns that overall averages hide.

Validating Findings With Staff

Before finalizing the analysis, Dana shared a draft with two nurses and a case manager and asked whether it matched their experience. They confirmed the scheduling gap and added that interpreter availability for Spanish-speaking patients was limited at discharge, a factor she added to the fishbone.

Conclusion

The data show a 23.1% readmission rate, most readmissions within 14 days, a strong association between scheduled follow-up and lower readmission, and fluid overload as the leading cause. Process mapping, interviews, a fishbone diagram and a Pareto ranking pointed to three main contributing factors. With national evidence supporting the pattern, the project moves to the literature to find proven interventions.

References

Hansen, L. O., Young, R. S., Hinami, K., Leung, A., & Williams, M. V. (2011). Interventions to reduce 30-day rehospitalization: A systematic review. Annals of Internal Medicine, 155(8), 520-528. https://doi.org/10.7326/0003-4819-155-8-201110180-00008

Hernandez, A. F., Greiner, M. A., Fonarow, G. C., Hammill, B. G., Heidenreich, P. A., Yancy, C. W., Peterson, E. D., & Curtis, L. H. (2010). Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA, 303(17), 1716-1722. https://doi.org/10.1001/jama.2010.533

Jencks, S. F., Williams, M. V., & Coleman, E. A. (2009). Rehospitalizations among patients in the Medicare fee-for-service program. New England Journal of Medicine, 360(14), 1418-1428. https://doi.org/10.1056/NEJMsa0803563