RESUMEN
The most effective readmission-prevention technologies include capabilities to: Accurately predict risk and stratify patients Synthesize compartmentalized data, both structured and unstructured, in real time and transform the data into actionable insights. Streamline workflow to shift time toward higher impact activities. Bridge care and communication across the continuum.
Asunto(s)
Tecnología Biomédica , Readmisión del Paciente/estadística & datos numéricos , Comunicación , Eficiencia Organizacional , Registros Electrónicos de Salud , Predicción , Humanos , Valor Predictivo de las Pruebas , Medición de Riesgo , Estados UnidosAsunto(s)
Atención a la Salud/organización & administración , Registros Electrónicos de Salud/organización & administración , Informática Médica/organización & administración , Atención a la Salud/tendencias , Registros Electrónicos de Salud/tendencias , Costos de la Atención en Salud/tendencias , Humanos , Informática Médica/tendencias , Investigación/organización & administración , Investigación/tendencias , Estados UnidosRESUMEN
In this paper we present risk-estimation models and methods for early detection of patient non-adherence based on unstructured text in patient records. The primary objectives are to perform early interventions on patients at risk of non-adherence and improve outcomes. We analyzed over 1.1 million visit notes corresponding to 30,095 Cancer patients, spread across 12 years of Oncology practice. Our risk analysis, based on a rich risk-factor dictionary, revealed that a staggering 30% of the patients were estimated to be at a high risk of non-adherence. Our risk classification showed that 2 distinct patient groups, between 26 and 38 (mean risk score, r=0.77, s=0.22), and 75 and 90 (r=0.81, s=0.19) years of age respectively, exhibited the highest risk of nonadherence when compared to the rest. The dominant risk-factors for these two groups, not surprisingly, included psychosocial (e.g. depression, lack of support), medical (e.g. side-effects such as pain) and financial issues (e.g. costs of treatment).