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ALHEChapter8ClinicalDecisionSupport.pptxChapter 8: Clinical Decision Support
Robert Hoyt MD
Harold Lehmann MD PhD
After reviewing these slides, the viewer should be able to:
Define electronic clinical decision support (CDS)
Enumerate the goals and potential benefits of CDS
Discuss the government and private organizations supporting CDS
Discuss CDS taxonomy, functionality and interoperability
List the challenges associated with CDS
Enumerate CDS implementation steps and lessons learned
Learning Objectives
Definition: “Clinical decision support (CDS) provides clinicians, staff, patients or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care.” (ONC)
Keep in mind that any resource that aids in decision making should be considered CDS. We will only consider electronic CDS.
We define clinical decision support systems (CDSSs) as the technology that supports CDS
Introduction
Early on, CDS was thought of only in terms of reminders and alerts. Now we must include diagnostic help, cost reminders, calculators, etc.
In spite of the fact that we can use the Internet’s potent search engines to answer questions, many organizations promote CDS as a major strategy to improve patient safety
Most CDS strategies involve the 5 rights (next slide)
Introduction
The right information (what): should be based on the highest level of evidence possible and adequately referenced.
To the right person (who): the person who is making the clinical decision, the physician, the patient or some other team member
In the right format (how): should the information appear as part of an alert, reminder, infobutton or order set?
Through the right channel (where): should the information be available as an EHR alert, a text message, email alert, etc.?
At the right time (when) : new information, particularly in the format of an alert should appear early in the order entry process so clinicians are aware of an issue before they complete the task
Five Rights of CDS
As early as the 1950s scientists predicted computers would aid medical decision making
CDS programs appeared in the 1970s and were standalone programs that eventually became inactive
De Dombal’s system for acute abdominal pain: used Bayes theorem to suggest differential diagnoses
Internist-1: CDS program that used IF-THEN statements to predict diagnoses
Mycin: rule-based system to suggest diagnosis and treatment of infections
Historical perspective
DxPlain: 1984 program that used clinical findings to list possible diagnoses. Now a commercial product
QMR: began as Internist-1 for diagnoses and ended in 2001
HELP: began in the 1980s at the University of Utah that includes diagnostic advice, references and clinical practice guidelines
Iliad: diagnostic program, also developed by the University of Utah in the 1980s
Historical perspective
Isabel: commercial differential diagnosis tool with information inputted as free text for from the EHR. Inference engine uses natural language processing and supported by 100,000 documents
SimulConsult: diagnostic program based on Bayes probabilities. Predictions can also include clinical and genetic information
SnapDx: free mobile app that performs diagnostic CDS for clinicians. It is based on positive and negative likelihood ratios from medical literature. App covers about 50 common medical scenarios
Historical perspective
CDS Benefits and Goals
| Benefits and Goals | Details |
| Improvement in patient safety | Medication alerts Improved ordering |
| Improvement in patient care | Improved patient outcomes Better chronic disease management Alerts for critical lab values, drug interactions and allergies Improved quality adjusted life years (QALY) |
| Reduction in healthcare costs | Fewer duplicate lab tests and images Fewer unnecessary tests ordered Avoidance of Medicare penalties for some readmissions Fewer medical errors Increased use of generic drugs Reduced malpractice |
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CDS Benefits and Goals
| CDS Benefits and Goals | Details |
| Dissemination of expert knowledge | Sharing of best evidence Education of all staff, students and patients |
| Management of complex clinical issues | Use of clinical practice guidelines, smart forms and order sets Interdisciplinary sharing of information Case management |
| Monitoring clinical details | Reminders for preventive services Tracking of diseases and referrals |
| Improvement of population health | Identification of high-cost/needs patients Mass customized messaging |
| Management of administrative complexity | Supports coding, authorization, referrals and care management |
| Support clinical research | Ability to identify prospective research subjects |
Institute of Medicine (IOM): they promoted “automated clinical information and CDS”
AMIA: developed 3 pillars of CDS in 2006—best available evidence, high adoption and effective use and continuous improvement.
ONC: has funded research to promote excellent CDS and sharing possibilities
AHRQ: also funded multiple CDS research projects and initiatives
Supporting Organizations
HL7: has a CDS working group and developed FHIR standards, discussed later
National Quality Forum (NQF): developed a CDS taxonomy
Leapfrog: they have promoted both CPOE and CDS
HIMSS: Their EMR Adoption Model rates EMRs from 0-7. Full use of CDS qualifies as level 6
CMS: Meaningful Use, Stage 1 and 2 includes CDS measures
Supporting Organizations
Two phases of CDS: knowledge use and knowledge management
Knowledge Use. Involves these sequential steps:
Triggers are an event, such as an order for a medication >>
Input data refers to information within, for example the EHR, that might include patient allergies >>
Interventions are the CDS actions such as displayed alerts >>
Action steps might be overriding the alert or canceling an order for a drug to which the patient is allergic
CDS Methodology
Knowledge management involves:
Knowledge acquisition: acquire expert internal (EHR data) or external data (e.g. Apache scores) for CDS
Knowledge representation. Use expert information, integrate it with an inference engine and communicate it to the end user, e.g. an alert (next slide)
Knowledge management (to follow)
CDS Methodology
Knowledge representation:
Configuration: knowledge is represented by choices made by the institution
Table-based: rules are stored in tables, such that if a current drug on a patient is in one row and an order for a second inappropriate drug is stored in the same row, an alert is triggered for the clinician
Rules based: knowledge base has IF-THEN statements; if the patient is allergic to sulfa and sulfa is order then an alert is triggered. Earlier CDS programs, such as Mycin, were rule based
CDS Methodology
Knowledge representation (Cont.)
Bayesian networks: based on Bayes Theorem of conditional probabilities it predicts future (posterior) probability based on pre-test probability or prevalence. In spite of assuming that the findings are supposed to be independent (such as signs and symptoms), the Bayesian approach works very well and is commonly employed in medicine. Formula is included below
CDS Methodology
The previous knowledge representation methods were based on known data so they would be labelled “knowledge based CDS”. If CDS is based on data mining-related techniques it would be referred to as “non-knowledge based CDS”
Data mining (machine learning) algorithms have to be developed and validated ahead of actual implementation. This approach is divided into supervised and unsupervised learning (next slide)
CDS Methodology
Supervised learning: assumes the user knows the categories of data that exist, such as gender, diagnoses, age, etc. If the target (outcome or dependent variable) is categorical (nominal, such as lived or died) the approach will be called a classification model. If the target is numerical (such as size of tumor, income, etc.) the this is a regression model (see chapter on Introduction to Data Science)
CDS Methodology
Supervised learning:
Neural networks: configured like a human neuron. The model is trained until the desired target output is close to the desired target. This is not intuitive and requires great expertise. See figure to the right
CDS Methodology
Supervised learning:
Logistic regression: in spite of the name regression it is most commonly used where the desired output/target is binary (cancer recurrence, no cancer recurrence). Multiple predictors are inputted, such as age, gender, family history, etc. and odds ratios are generated. This is the gold standard for much of predictive analytics
CDS Methodology
Decision trees: can perform classification or regression and are the easiest to understand and visualize. Trees are used by both statisticians and machine learning programs. Below is a contact lens decision tree
CDS Methodology
Unsupervised learning: means data is analyzed without first knowing the classes of data to look for new patterns of interest. This has been hugely important in looking at genetic data sets.
Cluster analysis is one of the most common ways to analyze large data sets for undiscovered trends. It is also more complex, requiring more expertise
Association algorithms look for relationships of interest
CDS Methodology
Knowledge maintenance: means there is a need to constantly update expert evidence based information. This task is difficult and may fall to a CDS committee or technology vendor
CDS Methodology
CDS developers have struggled for a long time with how to share knowledge representation with others or how to modify rules locally. Standards were developed to try to overcome these obstacles:
Arden syntax: represented by medical logic modules (MLMs) that encode decision information. Ironically, the information can’t be shared because institution specific coding resides within curly braces { } in the MLM. This approach was doomed and is known as the “curly brace problem”
CDS Standards
GELLO: can query EHRs for data to create decision criteria. Part of HL7 v. 3
GEM: permits clinical practice guidelines to be shared in an XML format, as an ASTM standard
GLIF: enables sharable and computable guidelines
CQL: draft HL7 standard to be used in XML format for electronic clinical quality measures (eCQMs)
Infobuttons: can be placed in workflow where decisions are made with recommendations
CDS Standards
Fast Healthcare Interoperability Resources (FHIR): developed by HL7 there is great hope that this standard will solve many interoperability issues.
It is a RESTful API (like Google uses) that uses either JSON or XML for data representation
It is data and not document centric; so a clinician could place a http request to retrieve just a lab value from EHR B, instead of e.g. a CCDA. EHR can also request decision support from software on a CDS server
Approximately, 95 resources have been developed to handle the most common clinical data issues
CDS Standards
CDSSs can be classified in multiple ways:
Knowledge and non-knowledge based systems
Internal or external to the EHR
Activation before, during or after a patient encounter
Activated automatically or on demand
Alerts can be interruptive or non-interruptive
The next slides show a taxonomy based on CDS goals and benefits mentioned earlier
CDS Functionality
CDS Functionality
CDS Functionality (Function and Examples cont.)
Ordering facilitators:
Order sets are EHR templated commercial or home grown orders that are modified to follow national practice guidelines. For example, a patient with a suspected heart attack has orders that automatically include aspirin, oxygen, EKG, etc.
Therapeutic support include commercial products such as TheradocⓇ and calculators for a variety of medical conditions
CDS Functionality
Order facilitators (cont.)
Smart forms are templated forms, generally used for specific conditions such as diabetes. They can include simple check the boxes with evidence based recommendations
Alerts and reminders are the classic CDS output that usually reminds clinicians about drug allergies, drug to drug interactions and preventive medicine reminders. This is discussed in more detail in the chapter on EHRs and the chapter on patient safety
CDS Functionality
Relevant information displays
Infobuttons, hyperlinks, mouse overs: common methods to connect to evidence based information
Diagnostic support: most diagnostic support is external and not integrated with the EHR; such as SimulConsult
Dashboards: can also be patient, and not population level, so they can summarize a patient’s status and thereby summarize and inform the clinician about multiple patient aspects
CDS Functionality
Currently, there is no single method for CDS knowledge can be universally shared. The approach has been to either use standards to share the knowledge or use CDS on a shared external server
Socratic Grid and OpenCDS are open source web services platforms that support CDS
The FHIR standard appears to have the greatest chance for success, but it is still early in the CDS game to know
CDS Sharing
CDS Implementation steps
CDS Implementation steps (cont.)
CDS Implementation steps (cont.)
General: exploding medical information that is complicated and evolving. Tough to write rules
Organizational support: CDS must be supported by leadership, IT and clinical staff. Currently, only large healthcare organizations can create robust CDSSs
Lack of a clear business case: evidence shows CDS helps improve processes but it is unclear if it affects behavior and patient outcomes. Therefore, there may not be a strong business case to invest in CDSSs
CDS Challenges
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Unintended consequences: alert fatigue
Medico-legal: adhering to or defying alerts has legal implications. Product liability for EHR vendors
Clinical: must fit clinician workflow and fit the 5 Rights
Technical: complex CDS requires an expert IT team
Lack of interoperability: must be solved for CDS to succeed
Long term CDS benefits: requires long term commitment and proof of benefit to be durable
CDS Challenges
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Lessons Learned
Lesson Learned (cont.)
This table came from multiple references found in the textbook
The future of Meaningful Use is unclear so there is no obvious CDS business case for clinicians, hospitals and vendors
If the FHIR standard makes interoperability easier we may see new CDS innovations and improved adoption
Future Trends
CDS could potentially assist with clinical decision making in multiple areas
While there is widespread support for CDS, there are a multitude of challenges
CDS is primarily achieved by larger healthcare systems
The evidence so far suggests that CDS improves patient processes and to a lesser degree clinical outcomes
Conclusions
