PCORI funded the Pilot Projects to explore how to conduct and use patient-centered outcomes research in ways that can better serve patients and the healthcare community. Learn more.
The use of decision support for symptom and quality of life (SQL) management is an innovative way to enhance patient engagement, facilitate patient–provider communication, and improve outcomes. Most clinical settings have not established efficient methods to collect SQL data and integrate decision support systems into the flow of care. Although multiple studies have assessed the usability and/or efficacy of decision support systems, little research has examined patient and caregiver perspectives about preferences for decision support program components.
The overall purpose of this study was to design and pilot test a patient-centered decision support program to process and manage SQL information in the cancer care setting. Phase I objectives were to (1) describe patient preferences for providing, processing, and managing SQL data that can enhance communication during the clinical encounter and (2) identify preferences for the format, specific information, and components for decision support that would be most useful to patients and their clinicians.
Phase II objectives of this study were to (1) develop three computable algorithms for patient self-assessment and management of pain, nausea and vomiting, and constipation; (2) develop a simulated model of a symptom assessment and management intervention for self-care (SAMI-SC); and (3) evaluate the usability and acceptability of a simulated model of SAMI-SC with adults recently treated for cancer, their caregivers, and clinicians.
Focus groups elicited information from patients, caregivers, and clinicians about their experiences with managing SQL data during cancer treatment and their preferences for desired components for decision support. An expert panel (cancer survivor, family caregiver, clinician, administrator, quality improvement specialist, and health equity officer) assisted in interpreting data, identifying potential interventions, and co-designing the intervention. Using a mixed methods sequential exploratory design, researchers built and iteratively tested usability and acceptability. Researchers evaluated the simulated SAMI-SC using focus groups, individual interviews, and questionnaires with patients, caregivers, and clinicians.
Participants, Interventions, Settings, and Outcomes
Sixty-four patient and caregiver participants (with a median age of 60, 61 percent female, 64 percent white, and 67 percent with college education) participated in Phase I focus groups. Fifty-one clinician participants (with a median age of 42 years, 76 percent female, 88 percent white, and an average of nine years of experience in cancer care) participated in Phase I interviews. Patients included individuals >18 years of age, English or Spanish speaking, treated for cancer within the previous six months. Enrolled patients were invited to identify caregivers (>18; English or Spanish speaking) to participate in the focus group. All participants were from the Boston-based Dana-Farber Cancer Institute (DFCI) network. Clinicians included medical doctors, physician assistants, nurse practitioners, or registered nurses in ambulatory settings within DFCI.
Researchers developed a simulated model of an algorithm-based decision support program and tested it in collaboration with the health communication core at Dana-Farber Harvard Cancer Center for self-management of pain, nausea and vomiting, and constipation. After patients responded to the questions in the program, a report provided specific suggestions for self-management of the symptom, guidance about when to call the patients’ clinicians, and a script for what to tell the clinicians.
In Phase I, researchers used a topic guide to prompt participants to discuss their experiences collecting, processing, and managing SQL during treatment for cancer and to identify preferred formats for decision support programs. In Phase II, researchers used a topic guide to conduct cognitive interviews with patients and caregivers to ensure that the wording of questions was understandable. A Likert-type response was presented to elicit feedback from patient, caregiver, and clinician participants. The a priori target for acceptability of SAMI-SC was a mean score of four or greater on each item.
In Phase I, transcripts were transcribed, de-identified and entered into NVivo. Three research team members conducted inductive content analysis of transcripts. In Phase II, patient, caregiver, and clinician demographic data were summarized using descriptive statistics. Acceptability E-scale results were summarized as means and standard deviations. Three study team members coded and grouped the data into themes from the audio-recorded session content. The research team combined themes (reconciled through discussion); created a list of suggested revisions to improve SAMI-SC algorithms; created the simulated model; and then ranked the list of suggested revisions to the simulated model, which were based on audio-recorded sessions and notes taken during those sessions. Researchers implemented critical revisions immediately and monitored less critical revisions for repetition, repeating this process until saturation (no new themes or changes).
Cognitive testing of all three algorithms showed that questions were easy to read and understand and identified barriers to use of decision support from patient and caregiver, clinician, or key stakeholders. Based on the results of the qualitative data, researchers identified five design objectives—ensure patient safety, communicate clinical concepts effectively, promote communication with clinicians, support patient activation, and facilitate navigation—that were relevant to the development of the algorithm-based CDS program. Patient and clinician acceptability surveys indicated strong support for the CDS program.
Data were from one health system but across affiliated centers (a comprehensive cancer center, a community-based center, and a federally qualified health center). Also, the sample consisted of adults with cancer and their caregivers who received treatment in an outpatient setting and findings cannot be generalized beyond this setting.
Patients and caregivers identified that an element of decision support missing in current systems was a mechanism to let them know when they should contact their clinicians about distressing symptoms and what to tell their clinicians. Researchers developed a program that addressed this need and that was acceptable to patients, caregivers, and clinicians.