Eliciting Requirements for Clinical Decision Support (CDS) Systems: A Case Study in Colorectal Cancer Care

Clinical Practice Guidelines (CPG) are published by healthcare organizations to provide clinicians with recommended standards of high-quality care. Several studies have reported low rates of compliance with CPGs resulting in variations in care, medical errors, and even iatrogenic harm to patients. Clinical Decision Support (CDS) systems are software that translate medical knowledge into computer-executable knowledge in order to improve guideline adherence at the point of care. The sources of medical knowledge include CPGs, predictive analytics models, and other forms of scientific evidence  such as findings from Genomics research. CDS systems are not designed to replace clinicians but instead provide cognitive support to clinicians in the form of treatment recommendations personalized for individual patients.

In this post, I discuss requirements elicitation for CDS systems. These systems are technologically complex but also interesting from a business analysis standpoint. I use the case of Colorectal cancer (CRC) because implementing CDS for CRC requires a seamless integration of clinical events, rules, processes, and predictive analytics models. I use the Decision Modeling Notation (DMN) to model the clinical decisions because it was defined exactly for that kind of purpose. First, I try to understand the business problems including prevalence in the population, medical errors in diagnosis, and the cost of medical malpractice claims.

Estimates of CRC Prevalence

In 2008, an estimated 1.24 millions new cases were diagnosed with CRC worldwide [1]. During the same year, an estimated  608,700 deaths were attributed to CRC.  According to the Center for Disease Control and Prevention (CDC), CRC is the second leading cause of cancer-related deaths in the United States and the third most common cancer in men and in women [2].  In 2011, there were an estimated 1,162,426 people living with CRC in the United States. Based on 2009-2011 data, approximately 4.7% of men and women will be diagnosed with CRC at some point during their lifetime. In 2014, estimated new cases of CRC were 136,830 in the U.S., with 50,310 deaths [3].

CRC Cases in Medical Malpractice Claims

Misdiagnosis in CRC cases is among the most common type of cancer-related medical malpractice claims in the US. For example, misdiagnosis can occur when a primary care provider fails to comply with existing CRC screening guidelines or fails to order diagnostic testing even when a patient presents with symptoms like rectal bleeding or unexplained anemia.

Genetic Testing

Patients with a family history of CRC are at high-risk. Lynch syndrome (also known as hereditary nonpolyposis colorectal cancer or HNPCC) is a well-defined inherited syndrome and is the most common form of genetic susceptibility to CRC. Germline mutations in DNA mismatch repair (MMR) genes (MLH1, MSH2, MSH6, and PMS2) are detected in up to 70-80% of families with HNPCC [4]. The National Comprehensive Cancer Network (NCCN) provides guidelines for the prevention, detection, and treatment of CRC including genetic testing and counseling for patients with CRC [5].

Predictive Analytics

Current guidelines recommend urgent referral based on symptoms like rectal bleeding or unexplained anemia and factors such as the patient's age and family history. Studies have shown that multivariable predictive analytics model such as the QCancer model developed in the United Kingdom can provide better performance than screening guidelines based on individual symptoms [6]. QCancer (Colorectal) was developed and internally validated on a large cohort of 3.6 million patients in primary care. QCancer returns a risk score for identifying individuals with undiagnosed CRC. This model can be used for risk stratification to provide early screening and prevention to patients at high risk of CRC.

CRC Decision Modeling with the DMN

Using the DMN, clinical decisions are represented as rule tasks within clinical workflows. The latter can be represented as business processes using the Business Process Modeling Notation (BPMN). Care pathways are often represented as treatment algorithms or decision trees in practice guidelines. In the DMN, the decision requirements model itself is represented using a notation called the Decision Requirements Diagram (DRD). The following is a DRD for a CRC decision model (click to enlarge).

The DRD notation provides various constructs for modeling decisions.

These constructs include:

• A Decision element denotes a clinical decision such as selecting a treatment option. The three primary treatment options are: surgery, chemotherapy, and radiation. The decision is made based on clinical input data and available clinical  knowledge (referred to as Business Knowledge Models in the DMN).


• Input data denotes data used as input to the clinical decision. In the case of CRC these data include all the data from the patient's electronic health record (EMR) that are predictor variables for the predictive analytics model as well as those that are required to make a clinical decision based on the treatment algorithms published by CRC guidelines. Examples include symptoms, demographics, body mass index, alcohol and smoking status, family history, genetic testing, and colonoscopy testing results. Note that the EHR should contain all clinical data about the patient. Other input data are shown in the diagram to emphasize specific clinical data need for CRC decision making.


• A Business Knowledge Model denotes a function that encapsulates clinical knowledge. This can take the form of a clinical rule, a decision table, or a predictive analytics model such as the QCancer model. An example of clinical rule based on genetic testing would be: If Patient has HNPCC/Lynch Syndrome, then refer to Endoscopist to perform colonoscopy starting at age 20-25.


• A Knowledge Source element denotes an authority for a Business Knowledge Model or Decision. In the case of CRC, the NCCN provides guidelines for the screening and treatment of CRC. A decision table has been derived from the treatment guidelines.


• Decision logic defines the specific logic used to make decisions in the form of business rules (for example using a business rule management system) or executable analytic models. Decision logic can be modeled using the DMN-defined Friendly Enough Expression Language (FEEL), the predictive model markup language (PMML), or if/then/else logic in any programming language.

References

[1] Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin 2011; 61:69.

[2] Center for Disease Control and Prevention. Colorectal Cancer Statistics. http://www.cdc.gov/cancer/colorectal/statistics/. Accessed April 5, 2015.

[3] National Cancer Institute (NCI). SEER Stat Fact Sheets: Colon and Rectum Cancer. http://seer.cancer.gov/statfacts/html/colorect.html. Accessed April 5, 2015.

[4] Peltomaki P. Role of DNA mismatch repair defects in the pathogenesis of human cancer. J Clin Oncol. 2003;21:1174–9.

[5] NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®). http://www.nccn.org/professionals/physician_gls/f_guidelines.ass. Accessed April 5, 2015

[6] Collins GS, Altman DG. Identifying patients with undetected colorectal cancer: an independent validation of QCancer (Colorectal) Br J Cancer. 2012;107(2):260–265. doi: 10.1038/bjc.2012.266

Three Priorities for the Business Analyst in 2015

In 2015, enterprises will continue to make significant investments in Analytics and Decision Management capabilities. The Business Analyst will play a key role in the development of software solutions that implement Business Processes, Business Rules, and Predictive Analytics.

The integration of Business Process Modeling and Decision Modeling

The Decision Modeling Notation (DMN) has finally been approved by the Object Management Group (OMG) in December 2014. The DMN will go mainstream in 2015. The DMN will be part of version 3 of the guide to the Business Analysis Body of Knowledge (BABOK Guide) of the International Institute of Business Analysis (IIBA). In addition, the update to the Business Intermediate Level of the OMG Certified Expert in BPM 2 (OCEB 2) certification now includes a module on the DMN.

Real world decision management scenarios typically include the integration of Business Process Modeling and Decision Modeling. An example is the implementation of Clinical Practice Guidelines (CPGs) and Care Pathways in Clinical Decision Support (CDS) systems. An integrated modeling approach ensures the seamless integration of clinical workflows and clinical rules and greater acceptance by clinicians.

Contributing to the Predictive Analytics Process

As companies continue to leverage their data assets to discover new knowledge through Predictive Analytics, the Business Analyst will collaborate closely with Data Scientists to facilitate the Analytics process. Specifically the Business Analyst will be responsible for the following:

• Elicit business requirements for the Analytics process.
• Analyze existing data sets to help the Data Scientist in understanding the data in the initial data exploration phase.
• Serve as a liaison between business stakeholders and Data Scientists.
• Analyze and document requirements for the effective integration of Analytics models with business processes, business rules, and business events.
• Effectively communicate the results of the Analytics process to business stakeholders.

For example, Predictive Analytics models can be used for risk stratification allowing healthcare providers to determine patients with a high risk of emergency admission to hospital or 30-day readmission. The Business Analyst can work with clinicians to elicit requirements for how to effectively deploy these models within clinical workflows.

Continuous Learning

Business Analysts will continue to expand their knowledge and skills to support their organizations in the face of rapid change in technology and the marketplace. The Cloud, Software as a Service (SaaS), Big Data Analytics, and the Internet of Things are technology trends to watch in 2015.

As Peter Senge wrote in his book titled The Fifth Discipline: The Art and Practice of the The Learning Organization:

The only sustainable competitive advantage is an organization's ability to learn faster than the competition.

Industry Credentials in Business Analysis

In a Deloitte's 2013 CIO Survey, 42% of CIOs rated business analysis as the top technical skills gap in their organization.

This week, I aced the Certification of Competency in Business Analysis™ (CCBA®) exam of the International Institute of Business Analysis (IIBA). The exam preparation was structured around mastering the six knowledge areas of A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide). The current official release of the BABOK® Guide is version 2.0 and contains a detailed description of generally accepted practices in the field of business analysis.

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Looking forward to version 3 of the  BABOK® Guide

I had a chance few months ago to participate in the pubic review of version 3 of the BABOK® Guide. The public review is now closed. Version 3 introduces the Business Analysis Framework (aka Turtle because of the shape of the diagram representing the framework) which describes the relationships between stakeholders, value, contexts, solutions, changes, and needs (see diagram below).

Version 3 includes the following perspectives:

• Agile
• Business Architecture
• Business Process Management
• Business Intelligence.

The CCBA exam based on version 3 of the BABOK® Guide will become available only sometime in 2015. It is an expanded version of version 2 and includes new techniques like the Decision Modeling Notation (DMN). See my previous post titled Modeling Clinical Rules with the Decision Modeling and Notation (DMN) Specification.