Computer-Based Clinical Decision Support Systems and Patient-Reported Outcomes: A Systematic Review

Blum, David; Raj, Sunil Xavier; Oberholzer, Rolf; Riphagen, Ingrid I.; Strasser, Florian; Kaasa, Stein

doi:10.1007/s40271-014-0100-1

Cited by 49 publications

(28 citation statements)

References 32 publications

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“…Patient reported outcomes (PROs) are of increasing interest both in the clinical trial [30] as well as the clinic [31] settings. Computer-based symptom surveys have been described and evaluated as a means of utilizing technology to better facilitate gathering PRO data and delivering it to the care team for clinical decision making [13][14][15][16].…”

Section: Discussionmentioning

confidence: 99%

Computerized patient-reported symptom assessment in radiotherapy: a pilot randomized, controlled trial

Fromme

Holliday

Nail

et al. 2015

Support Care Cancer

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Purpose Computer-based, patient-reported symptom survey tools have been described for patients undergoing chemotherapy. We hypothesized that patients undergoing radiotherapy might also benefit, so we developed a computer application to acquire symptom ratings from patients and generate summaries for use at point of care office visits and conducted a randomized-controlled pilot trial to test its feasibility. Methods Subjects were randomized prior to beginning radiotherapy. Both control and intervention group subjects completed the computerized symptom assessment, but only for the intervention group were printed symptom summaries made available before each weekly office visit. Metrics compared included the Global Distress Index (GDI), concordance of patient-reported symptoms and symptoms discussed by the physician and numbers of new and/or adjusted symptom management medications prescribed. Results 112 patients completed the study: 54 in the control and 58 in the intervention arms. There were no differences in GDI over time between the control and intervention groups. In the intervention group, more patient-reported symptoms were actually discussed in radiotherapy office visits: 46/202 vs. 19/230. A sensitivity analysis to account for within-subjects correlation yielded 23.2% vs. 10.3% (p=.03). Medications were started or adjusted at 15.4% (43/280) of control visits compared to 20.4% (65/319) of intervention visits (p=.07). Conclusions This computer application is easy to use and makes extensive patient-reported outcome data available at the point of care. Although no differences were seen in symptom trajectory, patients who had printed symptom summaries had improved communication during office visits and a trend towards more active symptom management during radiotherapy.

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Section: Discussionmentioning

confidence: 99%

Computerized patient-reported symptom assessment in radiotherapy: a pilot randomized, controlled trial

Fromme

Holliday

Nail

et al. 2015

Support Care Cancer

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“…Clinicians are interested to learn about the post-implementation impact of CDS tools, on different healthcare aspects, processes, and outcomes, before they consider their implementation in the clinical practice [64][65][66]. The most interesting part of the impact studies for clinicians is the effect size of the CDS tools and their direct impact on physicians' performance and patients' outcomes [67,68]. Clinicians consider that high quality experimental studies, such as randomised controlled trials, are the highest level of evidence, followed by observational well-designed cohort or case-control studies and lastly subjective studies, opinions of respected authorities, and reports of expert committees or panels [69][70][71].…”

Section: Post-implementation Impactmentioning

confidence: 99%

Validating and Updating GRASP: An Evidence-Based Framework for Grading and Assessment of Clinical Predictive Tools

Khalifa

Magrabi

Gallego

2020

Preprint

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Background: When selecting predictive tools, for implementation in their clinical practice or for recommendation in clinical guidelines, clinicians are challenged with an overwhelming and ever-growing number of tools. Many of these have never been implemented or evaluated for comparative effectiveness. To overcome this challenge, the authors developed an evidence-based framework for grading and assessment of predictive tools (GRASP), based on the critical appraisal of their published evidence. The objective of this study is to validate, update GRASP, and evaluate its reliability. Methods:The study is composed of two parts. The first includes validating and updating the GRASP framework and the second includes evaluating the framework reliability. For the first part, an online survey was developed to collect the responses of a wide international group of experts; identified as healthcare researchers who have published studies on developing, implementing or evaluating predictive tools and clinical decision support systems. For the second part, the interrater reliability of the framework, to assign grades to eight predictive tools by two independent users, will be evaluated.Results: Out of 882 invited experts, 81 valid responses were received. On a fivepoints Likert scale, experts overall strongly agreed to GRASP evaluation criteria of predictive tools (4.35/5). Experts strongly agreed to six criteria; predictive performance (4.87/5) and predictive performance levels (4.44/5), usability (4.68/5) and potential 2 effect (4.61/5), post-implementation impact (4.78/5) and evidence direction (4.26/5).Experts somewhat agreed to one criterion; post-implementation impact levels (4.16/5).Experts were neutral about one criterion; usability is higher than potential effect (2.97/5). Two thirds of the experts provided recommendations to six open-ended questions regarding adding, removing or changing evaluation criteria. Over half of the experts suggested that the potential effect, as an evaluation criterion, should be higher than the usability. Experts highlighted the importance of reporting the quality of studies and the strength of evidence supporting the grades assigned to predictive tools. The GRASP concept and its detailed report were updated based on experts' feedback.Following validation, the interrater reliability of the GRASP framework, to produce accurate and consistent results by two independent users, was tested and the framework found to be reliable. Answering open-ended questions, the two independent users reported the GRASP framework was logical, useful, and easy to use. Discussion and Conclusion:The GRASP framework grades predictive tools based on the critical appraisal of the published evidence across three dimensions: 1) Phase of evaluation; 2) Level of evidence; and 3) Direction of evidence. The final grade of a tool is based on the highest phase of evaluation, supported by the highest level of positive evidence, or mixed evidence that supports positive conclusion. GRASP is not meant to be prescriptive; it provi...

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“…Clinical decision support (CDS) systems are designed to directly support clinical decision-makers by using multi-source information to provide patient-centered assessments or guidance [28,29]. There is growing evidence that oncological and nononcological CDS systems appear to be well accepted and may support the diagnostic process, risk assessment, care plan, and treatment [30][31][32][33][34][35]. However, the implementation of CDS systems entails enormous technical, organizational, and cultural challenges [15].…”

Section: Restriction Of Current Solutionsmentioning

confidence: 99%

Structured Data Acquisition in Oncology

Henkel

Stieltjes

2019

Oncology

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Background: Demographic changes and improvement in therapy have shifted the focus of treatment towards chronic diseases and multiple health conditions. This has caused a tremendous increase in data per patient that needs to be integrated longitudinally and across departmental silos. The general increase in the volume of data per diagnostic examination and the number of diagnostic procedures per diagnostic pathway additionally accentuate this data integration challenge. Summary: Subspecialization in medicine has led to largely autonomously organized departments with independent IT ecosystems. This patchwork of IT infrastructure is not prepared to meet the data integration challenge. The resulting lack of integrated information makes the treatment of chronically ill patients increasingly difficult and error prone. Key Message: A sustainable method for data acquisition is needed to aid multimodal treatment and improve efficiency in healthcare.

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Computer-Based Clinical Decision Support Systems and Patient-Reported Outcomes: A Systematic Review

Abstract: There are marginal positive effects of CCDSS on specific PRO. Factors that facilitate the use and effect are identified. Easy to use systems with difficult to ignore evidence-based advice need to be developed and tested.

Cited by 49 publications

References 32 publications

Computerized patient-reported symptom assessment in radiotherapy: a pilot randomized, controlled trial

Computerized patient-reported symptom assessment in radiotherapy: a pilot randomized, controlled trial

Validating and Updating GRASP: An Evidence-Based Framework for Grading and Assessment of Clinical Predictive Tools

Structured Data Acquisition in Oncology

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