DEVELOPMENT OF A PATIENT-PREFERENCE-WEIGHTED SCORING ALGORITHM FOR THE QUALITY OF CARE FOR PATIENTS WITH ADVANCED ILLNESS (QCPAI) IN A MULTI-ETHNIC ASIAN SETTING

Author(s)

Mihir Gandhi, PhD1, Shir Lynn Lim, MBBS, MMed (Int Med), MRCP (UK)2, Su-Yen Tan, MBBS3, Si Yuan Chew, MBBS, GDFM, M Med (Int Med), MRCS (Ed), MRCP (UK)4, Poh Heng Chong, MBBS5, Shirlyn H.S. Neo, MBBS, MRCP (UK), M Med (Int. Med), FAMS6, Ru San Tan, MBBS, MRCP(UK)(Int Med)7, See Mieng Tan, PhD8, Barkat Sikder, MSc8, Felicia Ang, PhD8, Yin Bun Cheung, PhD9, Eric Finkelstein, MHA, PhD10;
1Duke-NUS Medical School, Lien Centre for Palliative Care and Centre for Biomedical Data Science, Singapore, Singapore, 2National University Hospital, Cardiology, Singapore, Singapore, 3Assisi Hospice, Singapore, Singapore, 4Singapore General Hospital, Respiratory Medicine, Singapore, Singapore, 5HCA Hospice Care, Singapore, Singapore, 6National Cancer Centre Singapore, Palliative Medicine, Singapore, Singapore, 7National Heart Centre Singapore, Cardiology, Singapore, Singapore, 8Duke-NUS Medical School, Lien Centre for Palliative Care, Singapore, Singapore, 9Duke-NUS Medical School, Centre for Biomedical Data Science, Singapore, Singapore, 10Duke-NUS Medical School Singapore, Lien Centre for Palliative Care, Singapore, Singapore
OBJECTIVES: The Quality of Care for Patients with Advanced Illness (QCPAI) is a patient-reported experience measure to assess the quality of care for those with advanced illnesses. This study aimed to develop a preference-weighted scoring algorithm for the QCPAI based on values from patients with advanced illness in multi-ethnic Singapore.
METHODS: We recruited patients with advanced illnesses across four hospitals and two hospices in Singapore. Patients completed the QCPAI and a discrete choice experiment to elicit their relative preferences across care domain levels. Preference weights were estimated using a latent-class logit model and rescaled to compute summary scores ranging from 0 (worst) to 100 (best).
RESULTS: Among 374 patients recruited (34.0% cancer, 29.9% heart disease, 16.3% respiratory disease, 18.2% renal disease, 1.6% others), 28.3% from hospices (73 daycare, 33 inpatient) and 71.7% from hospitals (161 inpatients, 107 outpatients). The most important domains for patients were alignment with treatment goals, appropriate medical care, and physical symptom management (relative importance 11.3%, 10.1% and 10.0%, respectively). Religious needs (2.1%) and in-patient visitor access (2.8%) were ranked lowest. Analysis of the preference curves indicated that patients do not exhibit diminishing returns to quality improvements, meaning they value improvements from poor to moderate quality similarly to improvements from moderate to high. The mean QCPAI score was 77.9 (SD 11.8). While 33.2% of patients received a score greater than 80, 9.8% experienced a score less than 70. Although most item-level scores indicated a positive experience (≥3 on a scale of 0-4), affordability (mean item 2.9) and waiting area times (mean 2.8) were rated as suboptimal.
CONCLUSIONS: The QCPAI provides a preference-weighted metric for benchmarking care for patients with advanced illnesses. It can be used to evaluate care quality over time within and across institutions and to quantify the effectiveness of interventions aimed at improving care quality for this vulnerable population.

Conference/Value in Health Info

2026-05, ISPOR 2026, Philadelphia, PA, USA

Value in Health, Volume 29, Issue S6

Code

HSD109

Topic

Health Service Delivery & Process of Care

Disease

SDC: Cardiovascular Disorders (including MI, Stroke, Circulatory), SDC: Oncology, SDC: Respiratory-Related Disorders (Allergy, Asthma, Smoking, Other Respiratory), SDC: Urinary/Kidney Disorders, STA: Multiple/Other Specialized Treatments

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