This post has been updated on February 15, 2015.
Despite its potential to improve the quality of care, CDS is not widely used in health care delivery today. In technology marketing parlance, CDS has not crossed the chasm. There are several issues that need to be addressed including:
- Clinicians' acceptance of the concept of automated execution of evidence-based clinical practice guidelines.
- Integration into clinical workflows and care protocols.
- Usability and human factors issues including alert fatigue and the human factors that influence alert acceptance.
- With the expanding use of clinical prediction models for diagnosis and prognosis, there is a need for a better understanding of the probabilistic approach to clinical decision making under uncertainty.
- Standardization to enable the interoperability and reuse of CDS knowledge artifacts and executable clinical guidelines.
- The challenges associated with the automatic concurrent execution of multiple clinical practice guidelines for patients with co-morbidities.
- Integration with modern information retrieval capabilities which allow clinicians to access up-to-date biomedical literature. These capabilities includes text mining, Natural Language Processing (NLP), and more advanced Clinical Question Answering (CQA) tools. CQA allows clinicians to ask clinical questions in natural language and extracts answers from very large amounts of unstructured sources of medical knowledge. PubMed has more than 23 millions citations for biomedical literature from MEDLINE, life science journals, and online books. The typical Clinical Practice Guideline (CPG) is 50 to 150 pages long. It is impossible for the human brain to keep up with that amount of knowledge.
- The use of mathematical simulations in CDS to explore and compare the outcomes of various treatment alternatives.
- Integration of genomics to enable personalized medicine as the cost of whole-genome sequencing (WGS) continues to fall.
- Integration of outcomes research in the context of a shift to a value-based healthcare delivery model. This can be achieved by incorporating the results of Comparative Effectiveness Research (CER) and Patient-Centered Outcome Research (PCOR) into CDS systems. Increasingly, outcomes research will be performed using observational studies (based on real world clinical data) which are recognized as complementary to randomized control trials (RCTs) for discovering what works and what doesn't work in practice. This is a form of Practice-Based Evidence (PBE) that is necessary to close the evidence loop.
- Support for a shared decision making process which takes into account the values, goals, and wishes of the patient.
- The use of Visual Analytics in CDS to facilitate analytical reasoning over very large amounts of structured and unstructured data sources.
- Finally, the challenges associated with developing hybrid decision support systems which seamlessly integrate all the technologies mentioned above including: machine learning predictive algorithms, real-time data stream mining, visual analytics, ontology reasoning, and text mining.
In response to a paper titled
Grand challenges in clinical decision support by Sittig et al. [1], Fox et al. [2] outlined four theoretical foundations for the design and implementation of CDS systems:
decision theory, theories of knowledge representation, process design, and organizational modeling. The practical approach discussed in this post is grounded in those four theoretical foundations.
CDS Interoperability
The complexity and cost inherent in capturing the medical knowledge in clinical guidelines and translating that knowledge into executable code remain a major impediment to the widespread adoption of CDS software. Therefore, there is a need for standards for the interchange and reuse of CDS knowledge artifacts and executable clinical guidelines.
Different formalisms, methodologies, and architectures have been proposed over the years for representing the medical knowledge in clinical guidelines. Examples include but are not limited to the following:
- The Arden Syntax
- GLIF (Guideline Interchange Format)
- GELLO (Guideline Expression Language Object-Oriented)
- GEM (Guidelines Element Model)
- The Web Ontology Language (OWL)
- PROforma
- EON
- PRODIGY
- Asbru
- GUIDE
- SAGE.
More recently, HL7 has published the Clinical Decision Support (CDS) Knowledge Artifact Specification which provides guidance on shareable CDS knowledge artifacts including event-condition-action rules, order sets, and documentation templates.
The HL7 Context-Aware Knowledge Retrieval (Infobutton) specifications provide a standard mechanism for clinical information systems to request context-specific clinical knowledge to satisfy clinicians and patients' information needs at the point of care.
Enabling the interoperability of executable clinical guidelines requires a standardized domain model for representing the medical information of patients and other contextual clinical information. The HL7 Virtual Medical Record (vMR) is a standardized domain model for representing the inputs and outputs of CDS systems. The ability to transform an HL7 CCDA document into an HL7 vMR document means that EHR systems that are Meaningful Use Stage 2 certified can consume these standard-compliant decision support services.
Because of the complexity and cost of developing CDS software, CDS software capabilities can be exposed as a set of services (part of a service-oriented architecture [16]) which can be consumed by other clinical systems such as EHR and Computerized Physician Order Entry (CPOE) systems. When deployed in the cloud, these CDS software services can be shared by several health care providers to reduce costs. The HL7 Decision Support Service (DSS) specification defines a REST and a SOAP web service interfaces using the vMR as message payload for accessing interoperable decision support services.
In practice, executable CDS rules (like other complex types of business rules) can be implemented with a production rule system using forward chaining. This is the approach taken by OpenCDS and some other large scale CDS implementations in real-world healthcare delivery settings. This allows CDS software developers to externalize the medical knowledge contained in clinical practice guidelines in the form of declarative rules as opposed to embedding that knowledge in procedural code. Many viable open source business rule management systems (BRMS) are available today and provide capabilities such as a rule authoring user interface, a rules repository, and a testing environment. Furthermore, a rule execution environment can be integrated with business processes, ontologies, and predictive analytics models (more on that later).
The W3C Rule Interchange Format (RIF) specification is a possible solution to the interchange of executable CDS rules. The RIF Production Rule Dialect (RIF PRD) is designed as a common XML serialization syntax for multiple rule languages to enable rule interchange between different BRMS. For example, RIF-PRD would allow the exchange of executable rules between existing BRMS like JBoss Drools, IBM ILOG JRules, and Jess. RIF is currently a W3C Recommendation and is backed by several BRMS vendors. Consentino et al. [3] described a model-driven approach for interoperability between IBM's proprietary ILOG rule language and RIF.
Seamless Integration into Clinical Workflows and Care Pathways
One of the main complaints against CDS systems is that they are not well integrated into clinical workflows and care protocols. Existing business process management standards like the Business Process Modeling Notation (BPMN) can provide a proven, practical, and adaptable approach to the integration of CDS rules and clinical pathways and protocols. Some existing open source and commercial BRMS already provide an integration of business rules and business processes out-of-the box and there are well-known patterns for integrating rules and processes [4, 5, 6] in business applications.
In 2014, the Object Management Group (OMG) released the Decision Model and Notation (DMN) specification which defines various constructs for modeling decision logic. The combination of BPMN and DMN [7, 8] provides a practical approach for modeling the decisions in clinical practice guidelines while integrating these decisions with clinical workflows. BPMN and DMN also support the modeling of decisions and processes that span functional and organizational boundaries.
Human Factors in the Use of Clinical Decision Support Systems
We need to do a better job in understanding the human factors that influence alert acceptance by clinicians in CDS. We also need clear and proven usability guidelines (backed by scientific research) that can be implemented by CDS software developers. Several research projects have sought to understand why clinicians accept or ignore alerts in medication-related CDS [9, 10]. Zacharia et al. [11] developed and validated an
Instrument for Evaluating Human-Factors Principles in Medication-Related Decision Support Alerts (I-MeDeSA). I-MeDeSA measures CDS alerts on the following nine human factors principles:
alarm philosophy, placement, visibility, prioritization, color learnability and confusability, text-based information, proximity of task components being displayed, and
corrective actions.
The British National Health Service (NHS) Common User Interface (CUI) Program has created standards and guidance in support of the usability of clinical applications with inputs from user interface design specialists, usability experts, and hundreds of clinicians with a diversity of background in using health information technology. The program is based on a rigorous development process which includes: research, design, prototyping, review, usability testing, and patient safety assessment by clinicians. In the US, the National Institute of Standards and Technology (NIST) has also published some guidance on the usability of clinical applications.
Studies have also shown that like in aviation, checklists can provide cognitive support to clinicians in the decision making process.
Integrating Genomic Data with CDS
The costs of whole-genome sequencing (WGS) and whole-exome sequencing (WES) continue to fall. Increasingly, both WGS and WES will be used in clinical practice for inherited disease risk assessment and pharmacogenomic findings [21]. There is a need for a modern CDS architecture that can support and facilitate the introduction and use of WGS and WES in clinical practice.
In a paper titled
Technical desiderata for the integration of genomic data with clinical decision support [14], Welch et al. proposed technical requirements for the integration of genomic data with clinical decision support. In another paper titled
A proposed clinical decision support architecture capable of supporting whole genome sequence information [15], Welch et al. proposed the following clinical decision support architecture for supporting whole genome sequence information (click to enlarge):
The proposed architecture includes the following components:
the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR). The authors suggest separating the genome data from the EHR data.
Practice-Based Evidence (PBE) needed for closing the evidence loop
Prospective randomized controlled trials (RCTs) are still considered the gold standard in evidence-based medicine. Although they can control for biases, RCTs are costly, time consuming, and must be performed under carefully controlled conditions.
The retrospective analysis of existing clinical data sources is increasingly recognized as complementary to RCTs for discovering what works and what doesn't work in real world clinical practice [23]. These retrospective studies will allow the creation of clinical prediction models which can provide personalized absolute risk and treatment outcome predictions for patients. They also facilitate what has been referred to as
"rapid learning health care." [24]
Toward Data-Driven Clinical Decision Support (CDS)
Williams Osler (1849-1919) [19] famously said that
"Medicine is a science of uncertainty and an art of probability."
The use of clinical prediction models for diagnosis and prognosis is becoming common practice in clinical care. These models can predict the health risks of patients based on their individual health data. Clinical Prediction Models provide absolute risk and treatment outcome prediction for conditions such as diabetes, kidney disease, cancer, cardiovascular disease, and depression. These models are built with statistical learning techniques and introduce new challenges related to their probabilistic approach to clinical decision making under uncertainty [20]. In his book titled
Super Crunchers: Why Thinking-By-Numbers Is The New Way To be Smart, Ian Ayres wrote:
Traditional experts make better decisions when they are provided with the results of statistical prediction. Those who cling to the authority of traditional experts tend to embrace the idea of combining the two forms of knowledge by giving the experts 'statistical support'. The purveyors of diagnostic software are careful to underscore that its purpose is only to provide support and suggestions. They want the ultimate decision and discretion to lie with the doctor. [12]
Furthermore, in order to leverage existing clinical domain knowledge from clinical practice guidelines and biomedical ontologies [22], machine learning algorithms' probabilistic approach to decision making under uncertainty must be complemented by technologies like production rule systems and ontology reasoners. Sesen et al. [18] designed a hybrid CDS for lung cancer care based on probabilistic reasoning with a Bayesian Network model and guideline-based recommendations using a domain ontology and an ontology reasoner.
Fox et al. [2] proposed an argumentation approach based on the construction, summarization, and prioritization of arguments for and against each generated candidate decision. These arguments can be both qualitative or quantitative in nature. On the importance of presenting evidence-based rationale in CDS systems, Fox et al. wrote:
In short, to improve usability of clinical user interfaces we advocate basing design around a firm theoretical understanding of the clinician’s perspective on the medical logic in a decision, the qualitative as well as quantitative aspects of the decision, and providing an evidence-based rationale for all recommendations offered by a CDS. [2]
In a paper titled
A canonical theory of dynamic decision-making [13], Fox et al. proposed a canonical theory of dynamic
decision-making and presented the PROforma clinical guideline modeling
language as an instance of the canonical theory.
Clinical predictive model presentation techniques include traditional score charts, nomograms, and clinical rules [17]. However Clinical Prediction Models are easier to use and maintain when deployed as scoring services (part of a service-oriented software architecture) and integrated into CDS systems. The scoring service can be deployed in the cloud to allow integration with multiple client clinical systems [20]. The Predictive Model Markup Language (PMML) specification published by the Data Mining Group (DMG) supports the interoperable deployment of predictive models in heterogeneous software environments.
Visual Analytics or data visualization techniques can also play an important role in the effective presentation of Clinical Prediction Models to nonstatisticians particularly in the context of shared decision making.
Concurrent execution of multiple guidelines for patients with co-morbidities
According to the Medicare 2012 chart book, "over two-thirds of beneficiaries having two or more chronic conditions and 14% having 6 or more chronic conditions." [25]
In
Grand Challenges in Clinical Decision Support [1], Sittig et al. wrote:
The challenge is to create mechanisms to identify and eliminate redundant, contraindicated, potentially discordant, or mutually exclusive guideline-based recommendations for patients presenting with co-morbid conditions.
Michalowski et al. [26] proposed a mitigation framework based on a first-order logic (FOL) approach.
A CDS Architecture for the era of Precision Medicine
I proposed a scalable CDS architecture for Precision Medicine in another post titled
Toward a Reference Architecture for Intelligent Systems in Clinical Care.
References
[1] Sittig D, Wright A, Osheroff JA, Middletone B, Jteich J, Ash J, et al. Grand challenges in clinical Decision support. J Biomed Inform 2008;41(2):387–92.
[2] Fox, J., Glasspool, D.W., Patkar, V., Austin, M., Black, L., South, M., et al. (2010). Delivering clinical decision support services: there is nothing as practical as a good theory. J. Biomed. Inform. 43, 831–843
[3] Valerio Cosentino, Marcos Didonet del Fabro, Adil El Ghali. A model driven approach for bridging ILOG Rule Language and RIF. RuleML, Aug 2012, Montpellier, France.
[4] Mauricio Salatino. (Processes & Rules) OR (Rules & Processes) 1/X. http://salaboy.com/2012/07/19/processes-rules-or-rules-processes-1x/. Retrieved February 15, 2015.
[5] Mauricio Salatino. (Processes & Rules) OR (Rules & Processes) 2/X. http://salaboy.com/2012/07/28/processes-rules-or-rules-processes-2x/. Retrieved February 15, 2015.
[6] Mauricio Salatino. (Processes & Rules) OR (Rules & Processes) 3/X. http://salaboy.com/2012/07/29/processes-rules-or-rules-processes-3x/. Retrieved February 15, 2015.
[7] Sylvie Dan. Modeling Clinical Rules with the Decision Modeling and Notation (DMN) Specification. http://sylviedanba.blogspot.com/2014/05/modeling-clinical-rules-with-decision.html. Retrieved February 15, 2015.
[8] Dennis Andrzejewski, Eberhard Beck, Eberhard Beck, Laura Tetzlaff. The transparent representation of medical decision structures based on the example of breast cancer treatment. 9th International Conference on Health Informatics.
[9] Phansalkar S, Zachariah M, Seidling HM, Mendes C, Volk L, Bates DW. Evaluation of medication alerts in electronic health records for compliance with human factors principles. J Am Med Inform Assoc. 2014 Oct;21(e2):e332-40. doi: 10.1136/amiajnl-2013-002279.
[10] Seidling HM, Phansalkar S, Seger DL, et al. Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support. J Am Med Inform Assoc 2011;18:479–84.
[11] Zachariah M, Phansalkar S, Seidling HM, et al. Development and preliminary evidence for the validity of an instrument assessing implementation of human-factors principles in medication-related decision-support systems--I-MeDeSA. J Am Med Inform Assoc 2011;18(Suppl 1):i62–72.
[12] Ayres I. Super Crunchers: Why Thinking-By-Numbers Is The New Way To be Smart. Bantam.
[13] Fox J., Cooper R. P., Glasspool D. W. (2013). A canonical theory of dynamic decision-making. Front. Psychol. 4:150 10.3389/fpsyg.2013.00150.
[14] Welch, B.M.; Eilbeck, K.; Del Fiol, G.; Meyer, L.; Kawamoto, K. Technical desiderata for the integration of genomic data with clinical decision support. 2014,
[15] Welch BM, Loya SR, Eilbeck K, Kawamoto K. A proposed clinical decision support architecture capable of supporting whole genome sequence information. J Pers Med. 2014 Apr 4;4(2):176-99. doi: 10.3390/jpm4020176.
[16] Loya SR, Kawamoto K, Chatwin C, Huser V. Service oriented architecture for clinical decision support: a systematic review and future directions. J Med Syst. 2014 Dec;38(12):140. doi: 10.1007/s10916-014-0140-z.
[17] Ewout W. Steyerberg. Clinical Prediction Models. A Practical Approach to Development, Validation, and Updating. New York: Springer, 2010.
[18] Sesen MB, Peake MD, Banares-Alcantara R, Tse D, Kadir T, Stanley R, Gleeson F, Brady M. 2014 Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care. J. R. Soc. Interface 11: 20140534. http://dx.doi.org/10.1098/rsif.2014.0534
[19] Bean RB, Bean, BW. Sir William Osler: aphorisms from his bedside teachings and writings. New York; 1950.
[20] Joel Amoussou. How good is your crystal ball?: Utility, Methodology, and Validity of Clinical Prediction Models. http://efasoft.blogspot.com/2015/01/how-good-is-your-crystal-ball-utility.html. Retrieved February 15, 2015.
[21] Dewey FE, Grove ME, Pan C, et al. Clinical Interpretation and Implications of Whole-Genome Sequencing. JAMA. 2014;311(10):1035-1045. doi:10.1001/jama.2014.1717.
[22] Joel Amoussou. Ontologies for Addiction and Mental Disease: Enabling Translational Research and Clinical Decision Support. http://efasoft.blogspot.com/2014/08/ontologies-for-addiction-and-mental.html. Retrieved February 2015.
[23] Future radiotherapy practice will be based on evidence from retrospective interrogation of linked clinical data sources rather than prospective randomized controlled clinical trials. Dekker, Andre L. A. J. and Gulliford, Sarah L. and Ebert, Martin A. and Orton, Colin G., Medical Physics, 41, 030601 (2014), DOI:http://dx.doi.org/10.1118/1.4832139
[24] Lambin, Philippe et al. 'Rapid Learning health care in oncology' – An approach towards decision support systems enabling customised radiotherapy. Radiotherapy and Oncology , Volume 109 , Issue 1 , 159 - 164.
[25] Centers for Medicare & Medicaid Services. Chronic Conditions Among Medicare Beneficiaries, Chartbook: 2012 Edition. http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Chronic-Conditions/Downloads/2012Chartbook.pdf. Accessed Feb. 15, 2015.
[26] Szymon Wilk, Martin Michalowski, Xing Tan, Wojtek Michalowski: Using First-Order Logic to Represent Clinical Practice Guidelines and to Mitigate Adverse Interactions. KR4HC@VSL 2014: 45-61.
Posts
