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Complex Clinical Decision Support May Improve Cancer Care

Researchers show that a clinical decision support system can be effective in managing multiple, complex symptoms in lung cancer patients.

By Sara Heath

Clinical decision support (CDS) systems may be able to enhance treatment for patients with multiple, complex symptoms, shows a recent study published in the Journal of Medical Internet Research.

The researchers sought to introduce a complex CDS algorithm to physicians treating patients with lung cancer. Considering the severity of lung cancer, the researchers explained that such advances could be critical to quality healthcare.

“Symptom management in lung cancer patients is complex, and uncontrolled symptoms have been associated with increased emotional distress, decreased health-related quality of life, and even decreased survival,” the researchers reported. “Optimal management requires attention to multiple symptoms.”

The study centered on the development and testing of the Symptom Assessment and Management Intervention (SAMI-L), a CDS tool geared toward mitigating five common symptoms in lung cancer patients. After patients enter their own list of symptoms and other relevant health information, the system generates potentially useful treatment actions for providers.

The study required three steps. First, the researchers worked with patients and providers for usability testing. This part of the study yielded several minor changes to the SAMI-L interface.

Second, the researchers tested the accuracy of different decision paths. Ultimately, this test yielded between 29 and 1425 accurate decision nodes, resulting in 19 to 3194 accurate, unique pathways per algorithm.

The final leg of the study was clinical testing to ensure that the tool was effective and acceptable in practice. Overall, both patients and providers liked the tool, stating that it improved the quality of care.

“The successful deployment of SAMI-L advances the field by demonstrating that complex clinical algorithms can be invoked in rule-based CDS systems to generate detailed patient-specific recommendations for use in the management of multiple symptoms at the point-of-care using patient-entered data,” the researchers reported.

This could be a significant advancement in health IT and CDS, the researchers suggested. Most CDS systems only generate decision pathways for one symptom. By integrating a set of multiple, complex systems, SAMI-L could allow providers to better deliver effective and targeted care to their patients.

“SAMI-L advances the field by supporting simultaneous management of multiple distressing symptoms in patients with lung cancer, in contrast to most previously reported systems that focus on a single symptom or problem,” the researchers explained.

“The SAMI-L system also incorporates a measurement-based approach using patient-reported symptom severity, age, comorbidities, laboratory values, and adherence to medications to instantiate symptom management algorithms that generate guidance for a report delivered to clinicians in real-time.”

The data entered into SAMI-L also produces real time management results, which allows providers to cater to immediate patient needs. This may ultimately improve quality of care and the patient experience.

Beyond highlighting the efficacy of a complex, multi-symptom CDS tool, the study also emphasizes a potential testing protocol. Specifically, the researchers said testing should be careful and iterative because the tool is so complex.

“The net result was more than a million possible unique data-parameter sets for traversing the most complex algorithm. The increased complexity of the logic supported by the SAMI-L CDS system necessitated new approaches to CDS testing,” the researchers explained. “This approach accommodated iterative testing of each protocol as it was refined by clinical experts, and allowed the testing process to be independent of the decision engine and the care protocol.”

In the future, the researchers say this tool may be adapted and tested in different clinics for patients with different forms of cancer. Because various types of cancer include their own unique and complicated set of symptoms, a system like SAMI-L may prove effective in improve patient care.

“Complex algorithms can be invoked through rule-based CDS systems to promote evidence-based care in real-time at the point of patient contact using current, patient-supplied information to generate explicit, detailed, and patient-specific care guidance,” the researchers concluded. “This information collected in real-time from patients can be used to inform the symptom management process and serve to prioritize management interventions.”

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