OBJECTIVES:  Increasingly, big-data electronic health record warehouses are used for developing and implementing high-risk identification algorithms for targeted readmission prevention programs (RPPs). However, the ability of these electronic tools to accurately detect the "appropriate" patients for RPP according to personal and clinical characteristics (termed "care sensitivity") has not yet been established. The aim of the study is to examine the ability of electronic readmission prediction risk tools to detect care-sensitive patients for inclusion in RPPs. METHODS:  Hospital physicians and nurses and primary care physicians and nurses were asked to complete a questionnaire on the clinical characteristics of discharged patients. The questionnaire assessed the degree to which each patient’s automated risk score for 30-day readmission was care-sensitive and the degree to which the patient should be included in RPPs. The correlations between hospitals' and clinics' healthcare provider's assessments and between physicians' and nurses' assessments were examined. RESULTS:  A total of 605 questionnaires regarding 276 patients were completed by physicians and nurses. Among patients with low risk scores (i.e., 0-39), both hospital physicians and clinic nurses found that 17% of the patients should have been included in RPPs whereas hospital nurses thought 28% should have been included. Among patients with high risk score (i.e., 50+), 17%, 28%, and 42% should not have been included in RPPs according to hospital nurses, hospital physicians, and clinic nurses, respectively. A significant correlation was found between hospital physicians and nurses regarding the assessment of patients’ risk scores (r=0.159, P=0.018) and the appropriateness for inclusion into RPPs (r=0.289, P<0.001). The most common reasons for patients to be included in RPPs were polypharmacy, the need for continuous monitoring, and low adherence. CONCLUSIONS: Combining electronic data with patients recorded characteristics allows for better adaptability and synchronization across different healthcare providers and for better selection of patients for inclusion in RPPs.