News and Events

Engaging Cancer Patients in Treatment Decision-Making

by Dr. Jim “Woody” Woodburn, MD MS

I recently read an excellent article and interview in the Journal of Clinical Pathways with Ellen Miller Sonet, chief strategy, and policy officer, CancerCare. It discusses the critical need to involve patients with cancer in treatment decision-making.

Variables cancer patients consider when comparing cancer treatment options

Summary 

Patients and their families have for years sought information on the outcomes of clinical therapies, side effects, out-of-pocket costs and a host of other personal and family-oriented questions to better inform their decisions on the cancer care they wish to receive.  In addition, there are resources that are available by the patient’s health plan, provider organization, community services, etc. that are hard to find or completely unknown to the patient. Add to all of this the remarkable pace of new clinical wisdom and cancer therapy protocols and you can understand why we need a methodology to be equal to the tidal wave of medical information, best practices and evolving benefits a cancer patient needs to understand.

I think Ellen Miller Sonet is correct. Applied Pathways’ position is physicians and patients should be able to weigh treatment options based on a combination of the best clinical evidence available and patient preferences.

A Solution

Technology can help make this possible. Applied Pathways provides the technological infrastructure to rapidly create, improve, and deliver highly contextualized patient-centered knowledge to better inform the patient and caregivers with evidence-based clinical, financial and social impacts of treatment options.  

Organizations like Mayo Clinic, AIM Specialty Health, and InformedDNA use this state-of-the-art visual programming and rules engine technology to continuously improve and deliver information at the point of time and point of need to people throughout the healthcare ecosystem. This system allows clinicians and experts to build assessments that ask the right questions and then build the logic to get to an answer or recommendation to the patient that builds upon and integrates needs and wishes in the critical dimensions of cost, side effects, efficacy, quality of life, logistics, etc.

Enabling Multi-Criteria Decision Analysis

Any one of the dimensions of consideration listed above is significantly complex in and of itself individually. Using a method such as Multi-Criteria Decision Analysis where all of the dimensions are weighted on a sliding scale based on the individual patient situation and preferences and then rolled up into summative conclusion as the best-fit clinical treatment regimen is a herculean task.  It requires overlapping and integrating dynamic answers to questions from an unlimited number of data sources that change the end result. Let me tell you how it works.

The approach to solving for this multivariate calculation is to break apart the dimensions into separate sets of assessments, empirically create a weighted score for each and then aggregate the results with specific recommendations based on the collective results.  The Applied Pathways platform uses this model of subset assessments and rules to interpret answer sets (we call them algorithms) and, using this type of subroutine programming model, uses overarching or mother algorithms to roll up complex sets into a cohesive and understandable report.

This set of assessments works well for a subjective and qualitative inquiry into patient and family member preferences, wishes and goals.  Two other dimensions, cost of care and clinical regimen/treatment plan choices, require access to other complex, dynamic and often times unclear data sets.

Cost of Care

Cost of care requires access to the health insurance benefits of the patient including plan specific information such as co-insurance, deductibles, in-network vs. out-of-network costs, and pharmacy benefits.  Patient out-of-pocket cost advice also needs to have access to current annual expenses already paid or accumulated and may cross annual coverage dates. These types of data collection rules can be built with the Applied Pathways visual programming and rules engine technology and our data integration services allow for straightforward data exchange with other systems in healthcare and elsewhere.

Clinical Regimen and Treatment Plan Choices

For the clinical regimen and treatment path options, one of our clients (AIM Specialty Health) has entire teams of clinical experts, informatics experts, analysts and coders who over many years have built excellent, evidence-based, physician-led clinical pathway algorithms that allow for a set of recommendations on the most appropriate treatment routines based upon precise clinical information data inputs.  These highly sophisticated rule sets are built for other clinical decision support applications such as what radiology imaging test is most appropriate but can handle oncology-specific decision support just as easily.

Final Thoughts

Cancer patients, their families, and caregivers deserve better information in order to make the best decisions possible in the challenging medical, financial, social and personal situations they face.  Technology can facilitate and simplify the assessments, inquiry, and display of options available to the patient when driven by rules created by experts and evidence.

Read MoreEngaging Cancer Patients in Treatment Decision-Making

Navigating the Hype of Healthcare Artificial Intelligence Companies

by Jennifer Bresnick

Healthcare artificial intelligence companies are offering intriguing products, but providers must be wary of too much hype around machine learning tools.

Few phrases in the healthcare IT world conjure up quite as much excitement as “artificial intelligence.” 

Sweeping through vendor marketing teams like a tidal wave of opportunity, nearly every technology company has at least thought about how they can integrate machine learning algorithms into their catalogues in order to take advantage of the fever-pitch hype around this new frontier of big data analytics.

Read the full story

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How Mayo Clinic Uses CDS Algorithms to Standardize Nurse Triage

An algorithmic approach to clinical decision support has standardized phone-based nurse triage at the Mayo Clinic.

By Jennifer Bresnick

 – The healthcare system has always relied on primary care providers to be the initial point of contact for patients with routine care needs and low-level complaints – a role that has become increasingly important as organizations try to cut costs, compete for shared savings, and proactively manage populations.

Read the full story here

 

Read MoreHow Mayo Clinic Uses CDS Algorithms to Standardize Nurse Triage