A qualitative analysis of algorithm-based decision support usability testing for symptom management across the trajectory of cancer care: one size does not fit all.

BACKGROUND: Adults with cancer experience symptoms that change across the disease trajectory. Due to the distress and cost associated with uncontrolled symptoms, improving symptom management is an important component of quality cancer care. Clinical decision support (CDS) is a promising strategy to integrate clinical practice guideline (CPG)-based symptom management recommendations at the point of care. METHODS: The objectives of this project were to develop and evaluate the usability of two symptom management algorithms (constipation and fatigue) across the trajectory of cancer care in patients with active disease treated in comprehensive or community cancer care settings to surveillance of cancer survivors in primary care practices. A modified ADAPTE process was used to develop algorithms based on national CPGs. Usability testing involved semi-structured interviews with clinicians from varied care settings, including comprehensive and community cancer centers, and primary care. The transcripts were analyzed with MAXQDA using Braun and Clarke's thematic analysis method. A cross tabs analysis was also performed to assess the prevalence of themes and subthemes by cancer care setting. RESULTS: A total of 17 clinicians (physicians, nurse practitioners, and physician assistants) were interviewed for usability testing. Three main themes emerged: (1) Algorithms as useful, (2) Symptom management differences, and (3) Different target end-users. The cross-tabs analysis demonstrated differences among care trajectories and settings that originated in the Symptom management differences theme. The sub-themes of "Differences between diseases" and "Differences between care trajectories" originated from participants working in a comprehensive cancer center, which tends to be disease-specific locations for patients on active treatment. Meanwhile, participants from primary care identified the sub-theme of "Differences in settings," indicating that symptom management strategies are care setting specific. CONCLUSIONS: While CDS can help promote evidence-based symptom management, systems providing care recommendations need to be specifically developed to fit patient characteristics and clinical context. Findings suggest that one set of algorithms will not be applicable throughout the entire cancer trajectory. Unique CDS for symptom management will be needed for patients who are cancer survivors being followed in primary care settings.

Algorithm-based decision support for symptom self-management among adults with Cancer: results of usability testing.

BACKGROUND: It 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. METHODS: This 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. RESULTS: In 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. CONCLUSION: Patient 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.

Long-term outcomes of the FRESH START trial: exploring the role of self-efficacy in cancer survivors' maintenance of dietary practices and physical activity.

BACKGROUND: This study examined whether changes in self-efficacy explain the effects of a mailed print intervention on long-term dietary practices of breast and prostate cancer survivors. The relationship between change in self-efficacy and long-term physical activity (PA) also was examined. METHODS: Breast and prostate cancer survivors (N = 543) from 39 US states and two Canadian provinces participated in the FRESH START intervention trial. Participants were randomly assigned to receive a 10-month program of mailed print materials on diet and PA available in the public domain or a 10-month program of tailored materials designed to increase fruit and vegetable (F&V) intake, decrease fat intake, and/or increase PA. Changes in self-efficacy for F&V intake and fat restriction were analyzed as potential mediators of the intervention's effects on diet at 2-year follow-up. Because we previously found that change in self-efficacy for PA did not vary by group assignment, the relationship between change in self-efficacy and PA at 2-year follow-up was examined across study conditions. RESULTS: Results suggest that change in self-efficacy for fat restriction partially explained the intervention's effect on fat intake (mean indirect effect = -0.28), and change in self-efficacy for F&V consumption partially explained the intervention's effect on daily F&V intake (mean indirect effect = .11). Change in self-efficacy for fat restriction partially accounted for the intervention's impact on overall diet quality among men only (mean indirect effect = 0.60). Finally, change in self-efficacy for PA predicted PA at 2-year follow-up. CONCLUSIONS: Findings suggest that self-efficacy may influence long-term maintenance of healthy lifestyle practices among cancer survivors.

A randomized trial of population-based clinical decision support to manage health and resource use for Medicaid beneficiaries.

To determine whether a clinical decision support system can favorably impact the delivery of emergency department and hospital services. Randomized clinical trial of three clinical decision support delivery modalities: email messages to care managers (email), printed reports to clinic administrators (report) and letters to patients (letter) conducted among 20,180 Medicaid beneficiaries in Durham County, North Carolina with follow-up through 9 months. Patients in the email group had fewer low-severity emergency department encounters vs. controls (8.1 vs. 10.6/100 enrollees, p < 0.001) with no increase in outpatient encounters or medical costs. Patients in the letter group had more outpatient encounters and greater outpatient and total medical costs. There were no treatment-related differences for patients in the reports group. Among patients <18 years, those in the email group had fewer low severity (7.6 vs. 10.6/100 enrollees, p < 0.001) and total emergency department encounters (18.3 vs. 23.5/100 enrollees, p < 0.001), and lower emergency department ($63 vs. $89, p = 0.002) and total medical costs ($1,736 vs. $2,207, p = 0.009). Patients who were ≥18 years in the letter group had greater outpatient medical costs. There were no intervention-related differences in patient-reported assessments of quality of life and medical care received. The effectiveness of clinical decision support messaging depended upon the delivery modality and patient age. Health IT interventions must be carefully evaluated to ensure that the resultant outcomes are aligned with expectations as interventions can have differing effects on clinical and economic outcomes.

Decision support for evidence-based pharmacotherapy detects adherence problems but does not impact medication use.

Although evidence-based pharmacotherapies are a principal component of patient care, 30-50% of patients do not take their medications as prescribed. We conducted a randomized trial of two clinical decision support (CDS) interventions in 2219 patients: patient adherence reports to providers (n=744), patient adherence reports to providers + email notices to care managers (n=736), and controls (739). At 18-month follow-up, there were no treatment-related differences in patient medication adherence (overall, by medication class, and by medical condition). There also were no treatment-related differences in patient clinical and economic outcomes. Thus, while this study's CDS information interventions were successfully delivered to providers and care managers, and were effective in identifying medication adherence deficits and in increasing care manager responses to medication adherences issues, these interventions were not able to alter patient medication behavior.

Implementation and evaluation of an electronic health record-integrated app for postpartum monitoring of hypertensive disorders of pregnancy using patient-contributed data collection.

Remote monitoring of women experiencing hypertensive disorders of pregnancy (HDP) can provide timely life-saving data, particularly if these data are integrated into existing patient and clinical workflows. This pilot intervention of a smartphone application (app) for postpartum monitoring of hypertensive disorders integrates patient-contributed data into electronic health records (EHRs) to support monitoring and clinical decision-making. Results from the evaluation of the pilot highlight the resources needed when implementing the app, challenges for integrating an app into the EHR, and the usability and utility of the HDP monitoring app for patient and clinician users. The implementation team's key observations included the importance of a local clinical champion, more robust patient involvement and support for the remote patient monitoring program, an impetus for EHR developers to adopt data integration standards, and a need to expand the capabilities of the standards to support interventions using patient-contributed data.

Population-based clinical decision support: a clinical and economic evaluation.

Governments are investing in health information technologies (HIT) to improve care quality and reduce medical costs. However, evidence of these benefits is limited. We conducted a randomized trial of three clinical decision support (CDS) interventions in 20,180 patients: email to care managers (n=3329), reports to primary care administrators (n=3368), letters to patients (n=3401), and controls (10,082). At 7-month follow-up, the letters to patients group had greater use of outpatient services and higher outpatient and total medical costs; whereas, the other groups had no change in clinical events or medical costs. As our CDS interventions were associated with no change or an increase in medical costs, it appears that investments in HIT without consideration for organizational context may not be sufficient to achieve improvements in clinical and economic outcomes.

The Technology Landscape of Patient-Centered Clinical Decision Support - Where Are We and What Is Needed?

Patient Centered Outcomes Research (PCOR) and health care delivery system transformation require investments in development of tools and techniques for rapid dissemination of clinical and operational best practices. This paper explores the current technology landscape for patient-centered clinical decision support (PC CDS) and what is needed to make it more shareable, standards-based, and publicly available with the goal of improving patient care and clinical outcomes. The landscape assessment used three sources of information: (1) a 22-member technical expert panel; (2) a literature review of peer-reviewed and grey literature; and (3) key informant interviews with PC CDS stakeholders. We identified ten salient technical considerations that span all phases of PC CDS development; our findings suggest there has been significant progress in the development and implementation of PC CDS but challenges remain.

Challenges and opportunities for advancing patient-centered clinical decision support: findings from a horizon scan.

OBJECTIVE: We conducted a horizon scan to (1) identify challenges in patient-centered clinical decision support (PC CDS) and (2) identify future directions for PC CDS. MATERIALS AND METHODS: We engaged a technical expert panel, conducted a scoping literature review, and interviewed key informants. We qualitatively analyzed literature and interview transcripts, mapping findings to the 4 phases for translating evidence into PC CDS interventions (Prioritizing, Authoring, Implementing, and Measuring) and to external factors. RESULTS: We identified 12 challenges for PC CDS development. Lack of patient input was identified as a critical challenge. The key informants noted that patient input is critical to prioritizing topics for PC CDS and to ensuring that CDS aligns with patients' routine behaviors. Lack of patient-centered terminology standards was viewed as a challenge in authoring PC CDS. We found a dearth of CDS studies that measured clinical outcomes, creating significant gaps in our understanding of PC CDS' impact. Across all phases of CDS development, there is a lack of patient and provider trust and limited attention to patients' and providers' concerns. DISCUSSION: These challenges suggest opportunities for advancing PC CDS. There are opportunities to develop industry-wide practices and standards to increase transparency, standardize terminologies, and incorporate patient input. There is also opportunity to engage patients throughout the PC CDS research process to ensure that outcome measures are relevant to their needs. CONCLUSION: Addressing these challenges and embracing these opportunities will help realize the promise of PC CDS-placing patients at the center of the healthcare system.

The technical landscape for patient-centered CDS: progress, gaps, and challenges.

Supporting healthcare decision-making that is patient-centered and evidence-based requires investments in the development of tools and techniques for dissemination of patient-centered outcomes research findings via methods such as clinical decision support (CDS). This article explores the technical landscape for patient-centered CDS (PC CDS) and the gaps in making PC CDS more shareable, standards-based, and publicly available, with the goal of improving patient care and clinical outcomes. This landscape assessment used: (1) a technical expert panel; (2) a literature review; and (3) interviews with 18 CDS stakeholders. We identified 7 salient technical considerations that span 5 phases of PC CDS development. While progress has been made in the technical landscape, the field must advance standards for translating clinical guidelines into PC CDS, the standardization of CDS insertion points into the clinical workflow, and processes to capture, standardize, and integrate patient-generated health data.

Integrating a Patient Engagement App into an Electronic Health Record-Enabled Workflow Using Interoperability Standards.

BACKGROUND: Patient use of mobile health applications is increasing. To promote patient-centered care, data from these apps must be integrated into clinician workflows within the electronic health record (EHR). Health Level 7 Fast Healthcare Interoperability Resources (FHIR) offers a standards-based application programming interface (API) that may support such integration. OBJECTIVE: We aimed to use interoperability standards to integrate a patient mobile application (coronavirus 2019 [COVID-19] Tracker) with an EHR. The COVID-19 Tracker engages patients by sending introductory and reminder text messages, collecting vital signs and symptom data from COVID-19 patients, and providing actionable guidance if concerning issues are identified. This case report explored the use of FHIR APIs to integrate the app into EHR-enabled clinical workflows. METHODS: The authors used notes from project meetings and from semistructured discussions among the application development team to track the design and implementation processes. Seven points of integration between the application and the EHR were identified, and approaches using FHIR to perform these integrations were delineated. RESULTS: Although this clinical decision support integration project benefited from its standards-based approach, many challenges were encountered. These were due to (1) partial implementation of the FHIR standard in the EHR, particularly, components needed for patient engagement applications; (2) limited experience with the adoption of FHIR standards; and (3) gaps in the current FHIR standard. Alternative approaches, often not based on interoperability standards, were developed to overcome these limitations. CONCLUSION: Despite the challenges encountered due to the early stages of FHIR development and adoption, FHIR standards provide a promising mechanism for overcoming longstanding barriers and facilitating the integration of patient engagement apps with EHRs. To accelerate the integration of apps into clinical workflows, additional components of the FHIR standard must be implemented within the EHR and other clinical systems. Continued expansion of available FHIR resources will help with tighter workflow integration.

Clinical Decision Support for Symptom Management in Lung Cancer Patients: A Group RCT.

CONTEXT: Clinical guidelines are available to enhance symptom management during cancer treatment but often are not used in the practice setting. Clinical decision support can facilitate the implementation and adherence to clinical guidelines. and improve the quality of cancer care. OBJECTIVES: Clinical decision support offers an innovative approach to integrate guideline-based symptom management into oncology care. This study evaluated the effect of clinical decision support-based recommendations on clinical management of symptoms and health-related quality of life (HR-QOL) among outpatients with lung cancer. METHODS: Twenty providers and 179 patients were allotted in group randomization to attention control (AC) or Symptom Assessment and Management Intervention (SAMI) arms. SAMI entailed patient-report of symptoms and delivery of recommendations to manage pain, fatigue, dyspnea, depression, and anxiety; AC entailed symptom reporting prior to the visit. Outcomes were collected at baseline, two, four and six-months. Adherence to recommendations was assessed through masked chart review. HR-QOL was measured by the Functional Assessment of Cancer Therapy-Lung questionnaire. Descriptive statistics with linear and logistic regression accounting for the clustering structure of the design and a modified chi-square test were used for analyses. RESULTS: Median age of patients was 63 years, 58% female, 88% white, and 32% ≤high school education. Significant differences in clinical management were evident in SAMI vs. AC for all target symptoms that passed threshold. Patients in SAMI were more likely to receive sustained-release opioids for constant pain, adjuvant medications for neuropathic pain, opioids for dyspnea, stimulants for fatigue and mental health referrals for anxiety. However, there were no statistically significant differences in HR-QOL at any time point. CONCLUSION: SAMI improved clinical management for all target symptoms but did not improve patient outcomes. A larger study is warranted to evaluate effectiveness.

Clinical and economic results from a randomized trial of clinical decision support in a rural health network.

BACKGROUND: Replication studies evaluate technologies in usual use settings. METHODS: We conducted a clinical trial to determine whether reductions in clinical and economic results observed in a previous study could be replicated in a larger setting. Subjects were randomized to receive intervention (email notifications for sentinel health events sent to their care managers) or control. MAIN OUTCOME MEASURES: The primary outcome was the rate of emergency department visits for low severity conditions. Secondary outcomes included: medical costs and other clinical event rates. RESULTS: We randomized 13,454 individuals (intervention, 6740; control, 6714). Subjects in both groups had similar rates of clinical events and medical costs. CONCLUSION: The use of email notifications to care managers was associated with no reductions in clinical events or medical costs.

Implementation of a clinical decision support system using a service model: results of a feasibility study.

Numerous studies have shown that the quality of health care is inadequate, and healthcare organizations are increasingly turning to clinical decision support systems (CDSS) to address this problem. In implementing CDSS, a highly promising architectural approach is the use of decision support services. However, there are few reported examples of successful implementations of operational CDSS using this approach. Here, we describe how Hospital Italiano de Buenos Aires evaluated the feasibility of using the SEBASTIAN clinical decision support Web service to implement a CDSS integrated with its electronic medical record system. The feasibility study consisted of three stages: first, end-user acceptability testing of the proposed CDSS through focus groups; second, the design and implementation of the system through integration of SEBASTIAN and the authoring of new rules; and finally, validation of system performance and accuracy. Through this study, we found that it is feasible to implement CDSS using a service-based approach. The CDSS is now under evaluation in a randomized controlled trial. The processes and lessons learned from this initiative are discussed.

A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine.

BACKGROUND: In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs. DISCUSSION: Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the Roadmap for National Action on Clinical Decision Support commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government. SUMMARY: A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.

A randomized clinical trial of clinical decision support in a rural community health network serving lower income individuals: study design and baseline characteristics.

Lower income individuals in the US frequently experience difficulties in obtaining access to needed health care services. We describe a randomized clinical trial that seeks to improve the quality of, and access to healthcare services for medically underserved populations in five rural counties of North Carolina. We propose to achieve these improvements by implementing system-to-system integration via a telehealth network with an asynchronous clinical decision support system for health care providers.

Change in self-efficacy partially mediates the effects of the FRESH START intervention on cancer survivors' dietary outcomes.

OBJECTIVE: This study examined change in self-efficacy as a mediator of the effects of a mailed print intervention on the dietary and exercise practices of newly diagnosed breast and prostate cancer survivors. METHOD: A total of 543 breast and prostate cancer patients were recruited from 39 states and two provinces within North America. Participants were randomly assigned to receive a 10-month program of tailored mailed print materials that aimed to increase fruit and vegetable consumption, reduce fat intake, and/or increase exercise or a 10-month program of publically available materials on diet and exercise. Telephone surveys conducted at baseline and 1 year assessed dietary practices, physical activity, and self-efficacy for engaging in these health behaviors. RESULTS: Results indicated that changes in self-efficacy for fat restriction and eating more fruits and vegetables were significant mediators of the intervention's effects on dietary outcomes at 1-year follow-up. The intervention did not significantly affect self-efficacy for exercise; however, a significant, positive relationship was found between self-efficacy for exercise and exercise duration at follow-up. CONCLUSIONS: Findings are largely consistent with Social Cognitive Theory and support the use of strategies to increase self-efficacy in health promotion interventions for cancer survivors.

Proposal for fulfilling strategic objectives of the U.S. Roadmap for national action on clinical decision support through a service-oriented architecture leveraging HL7 services.

Despite their demonstrated effectiveness, clinical decision support (CDS) systems are not widely used within the U.S. The Roadmap for National Action on Clinical Decision Support, published in June 2006 by the American Medical Informatics Association, identifies six strategic objectives for achieving widespread adoption of effective CDS capabilities. In this manuscript, we propose a Service-Oriented Architecture (SOA) for CDS that facilitates achievement of these six objectives. Within the proposed framework, CDS capabilities are implemented through the orchestration of independent software services whose interfaces are being standardized by Health Level 7 and the Object Management Group through their joint Healthcare Services Specification Project (HSSP). Core services within this framework include the HSSP Decision Support Service, the HSSP Common Terminology Service, and the HSSP Retrieve, Locate, and Update Service. Our experiences, and those of others, indicate that the proposed SOA approach to CDS could enable the widespread adoption of effective CDS within the U.S. health care system.

Research challenges for electronic health records.

The availability and use of electronic health records (EHRs) are likely to increase markedly over the next 10 years. This promulgation of EHRs will change dramatically the delivery of healthcare in the United States. Researchers seeking to maximize the impact of EHRs face at least two major and quite different challenges. First, rigorous evaluations of EHR systems are vital but not easily achieved. Second, researchers must determine how to take full advantage of the potential to create and disseminate new knowledge that is possible as a result of the data that are captured by EHRs. This paper reviews critical methodologic issues that need to be considered in the evaluation of EHRs, identifies pivotal policy issues that impact the ability of researchers (and public health professionals) to use the data that will accumulate as a result of EHR diffusion, and recommends actions for those who are interested in changing the landscape of EHR development, research, and implementation.

Development, deployment and usability of a point-of-care decision support system for chronic disease management using the recently-approved HL7 decision support service standard.

Clinical decision support is recognized as one potential remedy for the growing crisis in healthcare quality in the United States and other industrialized nations. While decision support systems have been shown to improve care quality and reduce errors, these systems are not widely available. This lack of availability arises in part because most decision support systems are not portable or scalable. The Health Level 7 international standard development organization recently adopted a draft standard known as the Decision Support Service standard to facilitate the implementation of clinical decision support systems using software services. In this paper, we report the first implementation of a clinical decision support system using this new standard. This system provides point-of-care chronic disease management for diabetes and other conditions and is deployed throughout a large regional health system. We also report process measures and usability data concerning the system. Use of the Decision Support Service standard provides a portable and scalable approach to clinical decision support that could facilitate the more extensive use of decision support systems.