Abstract

BackgroundIt is essential that cancer patients understand anticipated symptoms, how to self-manage these symptoms, and when to call their clinicians. However, patients are often ill-prepared to manage symptoms at home. Clinical decision support (CDS) is a potentially innovative way to provide information to patients where and when they need it. The purpose of this project was to design and evaluate a simulated model of an algorithm-based CDS program for self-management of cancer symptoms.MethodsThis study consisted of three phases; development of computable algorithms for self-management of cancer symptoms using a modified ADAPTE process, evaluation of a simulated model of the CDS program, and identification of design objectives and lessons learned from the evaluation of patient-centered CDS. In phase 1, algorithms for pain, constipation and nausea/vomiting were developed by an expert panel. In phase 2, we conducted usability testing of a simulated symptom assessment and management intervention for self-care (SAMI-Self-Care) CDS program involving focus groups, interviews and surveys with cancer patients, their caregivers and clinicians. The Acceptability E-scale measured acceptability of the program. In phase 3, we developed design objectives and identified barriers to uptake of patient-centered CDS based on the data gathered from stakeholders.ResultsIn phase 1, algorithms were reviewed and approved through a consensus meeting and majority vote. In phase 2, 24 patients & caregivers and 13 clinicians participated in the formative evaluation. Iterative changes were made in a simulated SAMI-Self-Care CDS program. Acceptability scores were high among patients, caregivers and clinicians. In phase 3, we formulated CDS design objectives, which included: 1) ensure patient safety, 2) communicate clinical concepts effectively, 3) promote communication with clinicians, 4) support patient activation, and 5) facilitate navigation and use. We identified patient barriers and clinician concerns to using CDS for symptom self-management, which were consistent with the chronic care model, a theoretical framework used to enhance patient-clinician communication and patient self-management.ConclusionPatient safety and tool navigation were critical features of CDS for patient self-management. Insights gleaned from this study may be used to inform the development of CDS resources for symptom self-management in patients with other chronic conditions.

Highlights

  • It is essential that cancer patients understand anticipated symptoms, how to self-manage these symptoms, and when to call their clinicians

  • The present study extends the literature by reporting on the design and formative evaluation of an algorithmbased simulated model of a patient-centered Clinical decision support (CDS) program that facilitates cancer symptom self-management, provides advice on when patients should contact their clinicians, and includes coaching information about what to tell them

  • This study was conducted at Dana-Farber Cancer Institute (DFCI) and approved by the Institutional Review Board (IRB), Protocol number-12-300

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Summary

Introduction

It is essential that cancer patients understand anticipated symptoms, how to self-manage these symptoms, and when to call their clinicians. Clinical decision support (CDS) is a potentially innovative way to provide information to patients where and when they need it The purpose of this project was to design and evaluate a simulated model of an algorithmbased CDS program for self-management of cancer symptoms. Evidence-based strategies to assist patients with selfmanagement include: education, telephone consultations, Internet tools for tracking disease-specific parameters, and coaching [3, 6,7,8,9,10,11,12,13] Such interventions targeted information management, medications, psychological consequences of illness, lifestyle, social support, communication, accessing services, and setting goals [14,15,16]. Even though symptoms of moderate to severe intensity were reported, clinicians did not provide patients with management guidance [10]

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