Chronic heart failure (CHF), chronic obstructive pulmonary disease (COPD), and pneumonia are among the top 10 causes of non-maternal, non-neonatal hospitalization, with an aggregate cost of $27.5 B.(1) It is challenging to properly manage the medical aspects of patients hospitalized with a single or overlapping diagnosis of CHF, COPD, and pneumonia because they share several risk factors. It is estimated that COPD is present in about 20% of patients with CHF.(2) COPD is associated with an increased risk of cardiovascular disease,(3) and a higher risk of hospitalization in CHF patients.(4) CHF is underrecognized and mistreated in patients with COPD.(5)
In patients with COPD, comorbid cardiovascular disease is an independent risk factor for developing pneumonia.(6) The probability of pneumonia is higher in COPD patients. Additionally, COPD as a comorbidity in pneumonia patients is associated with longer hospital length of stay (LOS) and worse outcomes. Despite that, pneumonia is still under-recognized in patients hospitalized with COPD.(7) This diagnostic challenge seems to prolong hospitalization LOS of CHF, COPD, and pneumonia, which average 5.4 days, 4.8 days, and 5.4 days, respectively.(8,9,10) In addition, it increases all causes 30-day readmission rates of CHF to 21–23%, COPD to 19%, and pneumonia to 17.9%.(11–13)
The following are additional challenges to treating patients hospitalized with CHF, COPD, and pneumonia.
Lack of Adherence to Guideline-Directed Medical Therapy (GDMT)
Research has shown that doctors are increasingly busy and unable to keep up with guidelines.(14) Despite the evidence that adherence to GDMT in CHF, COPD, and pneumonia yields better outcomes, evidence of poor GDMT adoption for all three diseases remains alarming.(16,17) Moreover, research shows that 28% of medical errors arise from individual providers’ cognitive errors, which may stem from a lack of medical knowledge.
Additionally, 46% of medical errors are driven by the synergistic effect of cognitive and system errors.(18) This vicious interplay between system and individual errors was echoed when one-third of the NEJM quality council reported their hospital’s quality and patient safety outcomes to be moderate, not very good, or poor.(19)
Lack of a Structured Team-Based Approach to Treat and Discharge Hospitalized Patients with CHF, COPD, and Pneumonia Within Budgeted LOS, and Deliver Value-Based Care
It is estimated that the U.S. healthcare system wastes about 25% of total healthcare costs.(20) Up to 25% of this waste could be prevented by process improvement and higher-quality healthcare delivery. Despite the established value of providing patient care as a team, such as integrated practice units (IPUs) to improve outcomes and reduce costs,(21) it’s still not widely adopted. Given the magnitude of the challenges, we designed and executed a suitable and sustainable solution.
THE GOAL
The goal of the solution was:
1. Operational efficiency by creating a collaborative clinical care pathway that includes steps that are critical to each team, excludes waste and wait, and provides a sensible, predictable, and repeatable plan for each admitted patient.
According to the European Pathway Association (EPA),(22) a clinical care pathway is: “A complex intervention for the mutual decision making and organization of care processes for a well-defined group of patients during a well-defined period.”
Clinical care pathways were shown to lower costs, reduce in-hospital complications, improve documentation, and potentially reduce length of stay and overall costs.(23)
2. Build an alignment culture where medical residents develop the ability to collaborate effectively with stakeholders from various disciplines, practice project management skills, manage each care pathway patient as a distinct project, and navigate its quadruple constraints of quality, time, cost, and scope.
3. Ensure each stakeholder is held accountable for the timely delivery of their assigned critical steps necessary to achieve their patient-related deliverables. A critical step in a project is one that, if delayed for any period of time, will delay the project delivery by the same period. The four patient-related deliverables we agreed to provide for our patients were:
Safe care: Governed by adherence to GDMT and characterized by low rates of adverse events, such as AKI, C. diff, and hospital-acquired complications.
Swift management: Governed by keeping LOS at less than four days and discharging patients before 11 a.m., provided it is safe to do so.
Satisfaction: As experienced by the patients.
Staying out of the hospital for 30 days post-discharge: Measured by keeping all-cause 30-day readmission rates below 20%.
THE EXECUTION
We followed Six Sigma methodology: DMAIC.
Define: We hypothesized that the root cause of the problem is the challenge of timely diagnosing admitted patients with CHF, COPD, and pneumonia, coupled with variable adherence to GDMT. We further hypothesized that this issue could be linked to the overutilization of resources, including time and consultants, and key stakeholders such as case managers and physical therapists. Hence, we decided to start with data-driven analysis.
Measure:
Hard data. We requested data on CHF, COPD, and pneumonia from our division to measure the mean length of stay (LOS). We asked for all available explanatory variables related to LOS. We measured the response rate of consultants and the impact of disposition location on LOS. We requested data on hospital-acquired complications.
Soft data. We interviewed key stakeholders and found that there was no structured process in place to assign time windows for specific stakeholders to complete specific critical steps. Additionally, there was no clear pathway outlining the dependencies of critical steps. We concluded that the fundamental problem was that most critical steps relied heavily on personal diligence and ad hoc planning.
Analyze: Our division’s data warehouse provided data on 1,035 CHF patients and 324 patients coded as COPD/pneumonia. A single regression analysis was performed, with LOS as the dependent variable. The average LOS for CHF patients was 5.2 days, and for COPD/pneumonia patients, 5 days.
Analysis of all explanatory variables available to us was significant. For CHF, there was a delay in ordering echocardiograms (average of 11 hours, r=0.2) and a delay in administering the first dose of Lasix (average of 7 hours, r=0.1). Although the correlation between the two variables was less than 0.3, we inferred that diagnosis at presentation was challenging. For COPD/pneumonia, there was an overuse of IV steroids (average of 2.3 days, r=0.5) and a delay in switching IV antibiotics to oral (average of 3.5 days, r=0.5). All results were statistically significant and appeared to contribute to prolonged LOS.
Across all patients, only 80% of consultants responded to consultations placed on the computer within 24 hours. Missing a single MRSA pneumonia case would result in a monetized loss of $52,000. About 20% of our patients were discharged to skilled facilities after an average LOS of eight days. We couldn’t obtain or measure any other specific data.
Improve: We harnessed the power of data and reliable literature sources to define an agreed-upon GDMT. We collaborated with subspecialty consultants and ancillary staff to obtain their buy-ins and input on the steps each team needs to take to deliver the four patient-related deliverables. We worked to sequence critical steps in a way that can realistically achieve our deliverables every time we manage a patient hospitalized with CHF, COPD, or pneumonia — a care pathway patient.
We enrolled all adult patients 18 and older admitted for CHF, COPD, pneumonia, or an overlapping presentation. The only exclusion criteria were ICU admissions and patients who left the hospital against medical advice.
Plan-Do-Study-Act Cycle 1
During our first PDSA cycle, which began in September 2023, we created a checklist (Figure 1) that outlined swim lanes for each stakeholder and timeboxed each critical step. This checklist was stationed on every floor to guide our daily multidisciplinary rounds (MDRs). After our first 75 patients, our results showed improvements as follows:
Average LOS: 3.6 days
Patients discharged before 11 a.m. (DBE): 41%
All-cause 30-day readmissions: 21%
Average number of consultants per case: 1.1
Adverse events: 4% of patients experienced AKI, while the rates for HA-MRSA falls, CAUTI, CLABSI, and C. diff were all zero.
We decided to build a computer order set with timely orders. Because our computer system is outdated, we collaborated with our IT team to create a complex order set that reflects our care pathway and ensures timely, structured orders (Figure 2).
PDSA Cycle 2
This control cycle ran from March 2024 to November 2024 and included an additional 125 patients, bringing the total to 200 patients. Results were demonstrably scalable, with minimal cognitive load on residents and ancillary staff as follows:
Average LOS: 4.3 days
Patients discharged before 11 a.m. (DBE): 44%
All-cause 30-day admissions: 26%
Average number of consultants per case: 1.4
Adverse events: 2% AKI, with no other adverse outcomes reported.
HURDLES AND LIMITATIONS
Data
We were unable to obtain sufficient baseline data to establish causality. Additionally, we could not track retrospective data on hospital-acquired complications. Despite these limitations, we utilized the available data to make the best possible statistical inference. Measuring soft data, such as patient satisfaction, proved impossible because of low patient survey response rates and the absence of alternative mechanisms.
Nursing
Our hospital had just hired about 700 new nurses, who were in the process of onboarding during the project. We were unable to reliably assign the critical step of educating patients by providing printed educational materials to nurses. Instead, our residents took ownership of the critical step of educating patients orally to improve their satisfaction and reduce their chances of readmission.
Case Management
We successfully educated the team on the importance of critical steps in a project. We explained that a critical step, if delayed by any amount of time, will result in an equal delay in the project’s delivery. Through collaboration with case management (CM) leadership and physical therapy leadership, we achieved a significant reduction in LOS for patients transitioning to facilities.
Consultants
We successfully controlled consultation times with subspecialists by ensuring that each consultant received a phone call or text upon consultation and daily follow-ups to maintain closed-loop communication. However, our hospital traditionally had weaker procedural coverage on weekends. While we were unable to alter the hospital-wide staffing model, we maintained an average of 1.3 consultations per admission.
Readmission Reduction
We were unable to procure a unit clerk or similar resource to confirm clinical appointments for patients — a critical step in achieving the low readmission deliverable. Additionally, we couldn’t procure health insurance for unfunded patients. We were, however, able to refer these patients to charity clinics, albeit with variable success.
THE TEAM
We are a community-based academic hospital. Our two teaching teams were responsible for executing the work day-to-day. These teams were led by our internal medicine (IM) residents on the inpatient service, working in collaboration with charge nurses, case managers, physical therapists, respiratory therapists, pharmacists, and the bed management team.
The care pathway was designed by the IM residency program director, with support from the chief medical officer (CMO), chief financial officer (CFO), case management (CM) director, associate program director, and IT director.
METRICS
We viewed value-based care through the lens of our value index (quality/cost). At the end of the project, we tracked our value index through the four deliverables outlined by the care pathway as follows:
Safe care: Our order set helped our providers opt into using GDMT and optimized the use of IV medications. Despite early and aggressive diuresis, only 2.5% of our patients had acute kidney injury. The rates of falls, hospital-acquired MRSA, C. diff, CAUTIs, and CLABSIs were zero.
Swift management: Our average LOS was four days, with 43% of patients discharged before 11 a.m. We optimized the use of our consultants, with an average of 1.3 consultants per admission. We noted the impact of consultants on LOS (Figure 3). We also noted the effect of disposition on LOS with a weighted average of 65% of patients going home and 19% going home with home health services. The remaining discharges were sent to facilities (Figure 4).
Satisfaction: We couldn’t hardwire a process to improve patient satisfaction or objectively measure patient satisfaction.
Staying out of the hospital: Despite our swift management and having about 20% unfunded patients with no means to have a primary doctor, our all-cause 30-day readmission rate remained at 24%.
WHERE TO START
Value-based care mostly hinges on quality and cost. When facing a value problem, start by identifying a dynamic value index (quality/cost). Middle managers should begin by analyzing all available hard data. Subsequently, they should invite all front-line stakeholders to assist in analyzing soft data and walking through each process. These steps are critical to bridge the gap between what is known and what is being done.
To establish vertical alignment between senior leadership and front-line stakeholders, middle managers must show stakeholders how their work contributes to the organizational value index. To create interdepartmental horizontal alignment among stakeholders, managers should create a safe space where stakeholders know that questions are the best leaders and ideas are the best bosses. Teams should align and co-create a visual care pathway that includes and sequences all stakeholders’ critical steps to ensure their deliverables are delivered on time.
If a team has a significant discrepancy between their circle of concern and their circle of influence — such as short staffing or infrastructure issues — support must be provided at the senior leadership level. This will help strengthen the alignment and sense of belonging.
With the mounting financial pressures and the rise of meritocracy and accountability as contingencies for financial growth in the healthcare system, hospitals should adopt care pathways that hold stakeholders accountable. Care pathways will also serve as an alignment of the psychological contract between teams, a contract that will deliver value-based care by increasing quality and decreasing costs.
KEY TAKE-AWAYS
Building and hardwiring a value-based care model can be achieved by hinging it on a strong foundation of aligned stakeholders who are following a data-backed clinical care pathway that includes time-bound steps to deliver well-communicated value outcomes.
Patients are best served with a team that includes ancillary staff to cover patients’ active and latent needs. This team is as strong as its weakest member.
Value delivered can be defined by each team’s unique value index, defined as quality divided by cost (quality/cost). The value index increases with higher quality and decreases with higher cost.
Cost reduction is predicated on controlling variable costs, with variability in timing and decisions being the biggest culprits for cost overruns.
Our clinical care pathway for hospitalized patients with CHF, COPD, and pneumonia helped us address patients’ needs as a team led by medical residents. It led to less variability in clinical decisions, greater adherence to GDMT, and a significant reduction in length of stay.
Acknowledgments: We would like to acknowledge the work of the following individuals: HCA Lawnwood IM residents; Ray LaRouque, MD, associate site medical director; Paul Cimoch, MD; and Mickey Smith, MBA, CEO, for being such a great mentor, adviser, and lifelong learner.
REFERENCES
McDermott KW, Roemer M. Most Frequent Principal Diagnoses for Inpatient Stays in U.S. Hospitals, 2018. HCUP Statistical Brief #277. July 2021. Agency for Healthcare Research and Quality. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb277-TopReasons-Hospital-Stays-2018.pdf .
Richardson A, Tolley E, Hartmann J, Reedus J, Bowlin B, Finch C, Sands CW, Self T. Evaluation of Chronic Obstructive Pulmonary Disease (COPD) and Reduced Ejection Fraction Heart Failure (HFrEF) Discharge Medication Prescribing: Is Drug Therapy Concordant With National Guidelines Associated with a Reduction in 30-Day Readmissions? Respir Med. 2016;119:135–140. https://doi.org/10.1016/j.rmed.2016.09.008 .
Cobb K, Kenyon J, Lu J, Krieger B, Perelas A, Nana-Sinkam P, Kim Y, Rodriguez-Miguelez P. COPD Is Associated with Increased Cardiovascular Disease Risk Independent of Phenotype. Respirology. 2024;29(12):1047–1057. https://doi.org/10.1111/resp.14799 .
Hawkins NM, Petrie MC, Jhund PS, Chalmers GW, Dunn FG, McMurray JJ. Heart Failure and Chronic Obstructive Pulmonary Disease: Diagnostic Pitfalls and Epidemiology. Eur J Heart Fail. 2009;11(2):130–139. https://doi.org/10.1093/eurjhf/hfn013 .
de Miguel-Díez J, Núñez Villota J, Santos Pérez S, Manito Lorite N, Alcázar Navarrete B, et al. Multidisciplinary Management of Patients with Chronic Obstructive Pulmonary Disease and Cardiovascular Disease. Arch. Bronconeumol. 2024; 60(4): 226–237. https://doi.org/10.1016/j.arbres.2024.01.013 .
Lin SH, Perng DW, Chen CP, Chai WH, Yeh CS, et al. Increased Risk of Community-Acquired Pneumonia in COPD Patients with Comorbid Cardiovascular Disease. Int J Chro Obstruct Pulmon Dis. 2016;11: 3051–3058. https://doi.org/10.2147/COPD.S115137 .
Restrepo MI, Mortensen EM, Pugh JA, Anzueto A. COPD Is Associated with Increased Mortality in Patients with Community-Acquired Pneumonia. Eur Respir J. 2006; 28(2): 346–351. https://doi.org/10.1183/09031936.06.00131905 .
Zilberberg MD, Nathanson BH, Sulham K, Mohr J, Goodwin MM, Shorr AF. Descriptive Epidemiology and Outcomes of Patients with Short Stay Hospitalizations for the Treatment of Congestive Heart Failure in the US. Clinicoecon Outcomes Res. 2023; 15: 139–149. https://doi.org/10.2147/ceor.s400882 .
Wier LM, Elixhauser A, Pfuntner A, Au DH. Overview of Hospitalizations Among Patients with COPD, 2008. HCUP Statistical Brief #106. February 2011. Agency for Healthcare Research and Quality. http://www.hcupus.ahrq.gov/reports/statbriefs/sb106.pdf .
University of Rochester Medical Center. Average Length of Stay - Pneumonia Quality Measures. UR Medicine, Highland Hospital. https://www.urmc.rochester.edu/highland/quality-of-care/pneumonia/pneu_stay .
Rizzuto N, Charles G, Knobf MT. Decreasing 30-Day Readmission Rates in Patients With Heart Failure. Crit Care Nurse. 2022;42(4): 13–19. https://doi.org/10.4037/ccn2022417 .
Di Chiara C, Sartori G, Fantin A, Castaldo N, Crisafulli E. Reducing Hospital Readmissions in Chronic Obstructive Pulmonary Disease Patients: Current Treatments and Preventive Strategies. Medicina. 2025;61(1): 97. https://doi.org/10.3390/medicina61010097
Choi S, Kim YJ, Lee SM, Kim KG. Predicting 30-day Readmissions in Pneumonia Patients Using Machine Learning and Residential Greenness. Digit Health. 2025;11, 20552076251325990. https://doi.org/10.1177/20552076251325990 .
Beauchemin M, Cohn E, Shelton RC. Implementation of Clinical Practice Guidelines in the Health Care Setting. ANS Adv Nurs Sci. 2019; 42(4): 307–324. https://doi.org/10.1097/ans.0000000000000263
Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2022;145(18). https://doi.org/10.1161/cir.0000000000001063 .
Diaz AM, Smith LM, Peterson AN, Kent ML, Vellian NJ. Impact of Pharmacist Inhaler Prescribing at Discharge for Chronic Obstructive Pulmonary Disease on Readmission Rates. Chronic Obstr Pulm Dis. 2025;12(1): 43–51. https://doi.org/10.15326/jcopdf.2024.0553 .
Vaugh VM, Gandhi TN, Chopra V, Petty LA, Giesler DL, et al. Antibiotic Overuse After Hospital Discharge: A Multi-Hospital Cohort Study. Clin Infect Dis. 2021;73(11): e4499–e4506. https://doi.org/10.1093/cid/ciaa1372 .
Graber ML, Franklin N, Gordon R. Diagnostic Error in Internal Medicine. Arch Intern Med. 2005;165(13): 1493–1499. https://doi.org/10.1001/archinte.165.13.1493 .
Kachalia A, Vanhaecht K. Quality and Patient Safety Improvement Is Never Finished. NEJM Catalyst. 2024;5(9). https://doi.org/10.1056/cat.24.0316 .
Porter M, Lee T. The Strategy That Will Fix Health Care. Harvard Business Review. October 2013. https://hbr.org/2013/10/the-strategy-that-will-fix-health-care
Teisberg E, Wallace S, O’Hara S. Defining and Implementing Value-Based Health Care: A Strategic Framework. Acad Med. 2020;95(5): 682–685. https://doi.org/10.1097/ACM.0000000000003122
Vanhaecht K. The Impact of Clinical Pathways on the Organization of Care Processes. PhD thesis. October 2007. https://www.researchgate.net/publication/228996808_The_impact_of_clinical_pathways_on_the_organisation_of_care_processes .
Rotter T, Baatenburg de Jong R, Lacko SE, Ronellenfitsch U, Kinsman L. Clinical Pathways as a Quality Strategy. In: Busse R, Klazinga N, Panteli D, et al., eds. Improving Healthcare Quality in Europe: Characteristics, Effectiveness and Implementation of Different Strategies. European Observatory on Health Systems and Policies. 2019; 12. https://www.ncbi.nlm.nih.gov/books/NBK549262/ .