Toward a Reference Architecture for Intelligent Systems in Clinical Care

A Software Architecture for Precision Medicine

Intelligent systems in clinical care leverage the latest innovations in machine learning, real-time data stream mining, visual analytics, natural language processing, ontologies, production rule systems, and cloud computing to provide clinicians with the best knowledge and information at the point of care for effective clinical decision making. In this post, I propose a unified open reference architecture that combines all these technologies into a hybrid cognitive system for clinical decision support. Indeed, truly intelligent systems are capable of reasoning. The goal is not to replace clinicians, but instead to provide them with cognitive support during clinical decision making. Furthermore, Intelligent Personal Assistants (IPAs) such as Apple's Siri, Google's Google Now, and Microsoft's Cortana have raised our expectations on how intelligent systems interact with users through voice and natural language.

In the strict sense of the term, a reference architecture should be abstracted away from concrete technology implementation. However in order to enable a better understanding of the proposed approach, I take liberty in explaining how available open source software can be used to realize the intent of the architecture. There is an urgent need for an open and interoperable architecture which can be deployed across devices and platforms. Unfortunately, this is not the case today with solutions like Apple's HealthKit and ResearchKit.

The specific open source software mentioned in this post can be substituted with other tools which provide similar capabilities. The following diagram is a depiction of the architecture (click to enlarge).

 

Clinical Data Sources

Clinical data sources are represented on the left of the architecture diagram. Examples include electronic medical record systems (EMR) commonly used in routine clinical care, clinical genome databases, genome variant knowledge bases, medical imaging databases, data from medical devices and wearable sensors, and unstructured data sources such as biomedical literature databases. The approach implements the Lambda Architecture enabling both batch and real-time data stream processing and mining.

Predictive Modeling, Real-Time Data Stream Mining, and Big Data Genomics

The back-end provides various tools and frameworks for advanced analytics and decision management. The analytics workbench includes tools for creating predictive models and data streaming mining. The decision management workbench includes a production rule system (providing seamless integration with clinical events and processes) and an ontology editor.

The incoming clinical data likely meet the Big Data criteria of volume, velocity, and variety (this is particularly true for physiological time series from wearable sensors). Therefore, specialized frameworks for large scale cluster computing like Apache Spark are used to analyze and process the data. Statistical computing and Machine Learning tools like R are used here as well. The goal is knowledge and patterns discovery using Machine Learning model builders like Decision Trees, k-Means Clustering, Logistic Regression, Support Vector Machines (SVMs), Bayesian Networks, Neural Networks, and the more recent Deep Learning techniques. The latter hold great promise in applications such as Natural Language Processing (NLP), medical image analysis, and speech recognition.

These Machine Learning algorithms can support diagnosis, prognosis, simulation, anomaly detection, care alerting, and care planning. For example, anomaly detection can be performed at scale using the k-means clustering machine learning algorithm in Apache Spark. In addition, Apache Spark allows the implementation of the Lambda Architecture and can also be used for genome Big Data analysis at scale.

In another post titled How Good is Your Crystal Ball?: Utility, Methodology, and Validity of Clinical Prediction Models, I discuss quantitative measures of performance for clinical prediction models.

Visual Analytics

Visual Analytics tools like D3.js, rCharts, ploty, googleVis, ggplot2, and ggvis can help obtain deep insight for effective understanding, reasoning, and decision making through the visual exploration of massive, complex, and often ambiguous data. Of particular interest is Visual Analytics of real-time data streams like physiological time series. As a multidisciplinary field, Visual Analytics combines several disciplines such as human perception and cognition, interactive graphic design, statistical computing, data mining, spatio-temporal data analysis, and even Art. For example, similar to Minard's map of the Russian Campaign of 1812-1813 (see graphic below), Visual Analytics can help in comparing different interventions and care pathways and their respective clinical outcomes over a certain period of time by displaying causes, variables, comparisons, and explanations.

Production Rule System, Ontology Reasoning, and NLP

The architecture also includes a production rule engine and an ontology editor (Drools and Protégé respectively). This is done in order to leverage existing clinical domain knowledge available from clinical practice guidelines (CPGs) and biomedical ontologies like SNOMED CT.  This approach complements machine learning algorithms' probabilistic approach to clinical decision making under uncertainty. The production rule system can translate CPGs into executable rules which are fully integrated with clinical processes (workflows) and events. The ontologies can provide automated reasoning capabilities for decision support.

NLP includes capabilities such as:

• Text classification, text clustering, document and passage retrieval, text summarization, and more advanced clinical question answering (CQA) capabilities which can be useful for satisfying clinicians' information needs at the point of care; and
• Named entity recognition (NER) for extracting concepts from clinical notes.

The data tier supports the efficient storage of large amounts of time series data and is implemented with tools like Cassandra and HBase. The system can run in the cloud, for example using the Amazon Elastic Compute Cloud (EC2). For real-time processing of distributed data streams, cloud-based solutions like Amazon Kinesis and Lambda can be used.

 

Clinical Decision Services

The clinical decision services provide intelligence at the point of care typically using deployed predictive models, clinical rules, text mining outputs, and ontology reasoners. For example, Machine Learning algorithms can be exported in predictive markup language (PMML) format for run-time scoring based on the clinical data of individual patients, enabling what is referred to as Personalized Medicine. Clinical decision services include:

• Diagnosis and prognosis
• Simulation
• Anomaly detection 
• Data visualization
• Information retrieval (e.g., clinical question answering)
• Alerts and reminders
• Support for care planning processes.

The clinical decision services can be deployed in the cloud as well. Other clinical systems can consume these services through a SOAP or REST-based web service interface (using the HL7 vMR and DSS specifications for interoperability) and single sign-on (SSO) standards like SAML2 and OpenID Connect.

Intelligent Personal Assistants (IPAs)

Clinical decision services can also be delivered to patients and clinicians through IPAs. IPAs can accept inputs in the form of voice, images, and user's context and respond in natural language. IPAs are also expanding to wearable technologies such as smart watches and glasses. The precision of speech recognition, natural language processing, and computer vision is improving rapidly with the adoption of Deep Learning techniques and tools. Accelerated hardware technologies like GPUs and FPGAs are improving the performance and reducing the cost of deploying these systems at scale.

Hexagonal, Reactive, and Secure Architecture

Intelligent Health IT systems are not just capable of discovering knowledge and patterns in data. They are also scalable, resilient, responsive, and secure. To achieve these objectives, several architectural patterns have emerged during the last few years:

• Domain Driven Design (DDD) puts the emphasis on the core domain and domain logic and recommends a layered architecture (typically user interface, application, domain, and infrastructure) with each layer having well defined responsibilities and interfaces for interacting with other layers. Models exist within "bounded contexts". These "bounded contexts" communicate with each other typically through messaging and web services using HL7 standards for interoperability.


• The Hexagonal Architecture defines "ports and adapters" as a way to design, develop, and test an application in a way that is independent of the various clients, devices, transport protocols (HTTP, REST, SOAP, MQTT, etc.), and even databases that could be used to consume its services in the future. This is particularly important in the era of the Internet of Things in healthcare.


• Microservices consist in decomposing large monolithic applications into smaller services following good old principles of service-oriented design and single responsibility to achieve modularity, maintainability, scalability, and ease of deployment (for example, using Docker).


• CQRS/ES: Command Query Responsibility Segregation (CQRS) and Event Sourcing (ES) are two architectural patterns which consist in the use of event-driven messaging and an Event Store for separating commands (write-side) from queries (read-side) relying on the principle of Eventual Consistency. CQRS/ES can be implemented in combination with microservices to deliver new capabilities such as temporal queries, behavioral analysis, complex audit logs, and real-time notifications and alerts.


• Functional Programming: Functional Programming languages like Scala have several benefits that are particularly important for applying Machine Learning algorithms on large data sets. Like functions in mathematics, functions in Scala have no side effects. This provides referential transparency. Machine Learning algorithms are in fact based on Linear Algebra and Calculus. Scala supports high-order functions as well. Variables are immutable witch greatly simplifies concurrency. For all those reasons, Machine Learning libraries like Apache Mahout have embraced Scala, moving away from the Java MapReduce paradigm.


• Reactive Architecture: The Reactive Manifesto makes the case for a new breed of applications called "Reactive Applications". According to the manifesto, the Reactive Application architecture allows developers to build "systems that are event-driven, scalable, resilient, and responsive."  Leading frameworks that support Reactive Programming include Akka and RxJava. The latter is a library for composing asynchronous and event-based programs using observable sequences. RxJava is a Java port (with a Scala adaptor) of the original Rx (Reactive Extensions) for .NET created by Erik Meijer.
  
  Based on the Actor Model and built in Scala, Akka is a framework for building highly concurrent, asynchronous, distributed, and fault tolerant event-driven applications on the JVM. Akka offers location transparency, fault tolerance, asynchronous message passing, and a non-deterministic share-nothing architecture. Akka Cluster provides a fault-tolerant decentralized peer-to-peer based cluster membership service with no single point of failure or single point of bottleneck.
  
  Also built with Scala, Apache Kafka is a scalable message broker which provides high-throughput, fault-tolerance, built-in partitioning, and replication  for processing real-time data streams. In the reference architecture, the ingestion layer is implemented with Akka and Apache Kafka.

• Web Application Security: special attention is given to security across all layers, notably the proper implementation of authentication, authorization, encryption, and audit logging. The implementation of security is also driven by deep knowledge of application security patterns, threat modeling, and enforcing security best practices (e.g., OWASP Top Ten and CWE/SANS Top 25 Most Dangerous Software Errors) as part of the continuous delivery process.

An Interface that Works across Devices and Platforms

The front-end uses a Mobile First approach and a Single Page Application (SPA) architecture with Javascript-based frameworks like AngularJS to create very responsive user experiences. It also allows us to bring the following software engineering best practices to the front-end:

• Dependency Injection
• Test-Driven Development (Jasmine, Karma, PhantomJS)
• Package Management (Bower or npm)
• Build system and Continuous Integration (Grunt or Gulp.js)
• Static Code Analysis (JSLint and JSHint), and 
• End-to-End Testing (Protractor). 

For mobile devices, Apache Cordova can be used to access native functions when desired. The main goal is to provide a user interface that works across devices and platforms such as iOS, Android, and Windows Phone.

Interoperability

Interoperability will always be a key requirement in clinical systems. Interoperability is needed between all players in the healthcare ecosystem including providers, payers, labs, knowledge artifact developers, quality measure developers, and public health agencies like the CDC. These standards exist today and are implementation-ready. However, only health IT buyers have the leverage to demand interoperability from their vendors.

Standards related to clinical decision support (CDS) include:

• The HL7 Fast Healthcare Interoperability Resources (FHIR)
• The HL7 virtual Medical Record (vMR)
• The HL7 Decision Support Services (DSS) specification
• The HL7 CDS Knowledge Artifact specification
• The DMG Predictive Model Markup Language (PMML) specification.

Overcoming Barriers to Adoption

In a previous post, I discussed a practical approach to addressing challenges to the adoption of clinical decision support (CDS) systems.

Ontologies for Addiction and Mental Disease: Enabling Translational Research and Clinical Decision Support

In a previous post titled Why do we need ontologies in healthcare applications, I elaborated on what ontologies are and why they are different from information models of data structures like relational database schemas and XML schemas commonly used in healthcare informatics applications. In this post, I discuss two interesting applications of ontology engineering related to addiction and mental disease treatment. The first is the use of ontologies for achieving semantic interoperability in  translational research. The second is the use of ontologies for modeling complex medical knowledge in clinical practice guidelines (CPGs) for the purpose of automated reasoning during execution in clinical decision support systems (CDS) at the point of care.

Why Semantic Interoperability is needed in biomedical translational research?

In order to accelerate the discovery of new effective therapeutics for mental health and addiction treatment, there is a need to integrate data across disciplines spanning biomedical research and clinical care delivery [1]. For example, linking data across disciplines can facilitate a better understanding of treatment response variability among patients in addiction treatment. These disciplines include:

• Genetics, the study of genes.
• Chemistry, the study of chemical compounds including substances of abuse like heroin.
• Neuroscience, the study of the nervous system and the brain (addiction is a chronic disease of the brain)
• Psychiatry which is focused on the diagnosis, treatment, and prevention of addiction and mental disorders.

Each of these disciplines has its own terminology or controlled vocabularies. In the clinical domain for example, DSM5 and RrxNorm are used for documenting clinical care. In biomedical research, several ontologies have been developed over the last few years including:

• The Gene Ontology (GO)
• The Chemical Entities of Biological Interest Ontology (CHEBI)
• NeuroLex, an OWL ontology covering major domains of neuroscience: anatomy, cell, subcellular, molecule, function, and dysfunction.

To facilitate semantic interoperability between these ontologies, there are best practices established by the Open Biomedical Ontology (OBO) community. An example of best practice is the use of an upper-level ontology called the Basic Formal Ontology (BFO) which acts as a common foundational ontology upon which  new ontologies can be created. OBO ontologies and principles are available on the OBO Foundry web site.

Among the ontologies available on the OBO Foundry is the Mental Functioning Ontology (MF) [2, 3]. The MF is being developed as a collaboration between the University of Geneva in Switzerland and the University at Buffalo in the United States. The project also includes a Mental Disease Ontology (MD) which extends the MF and the Ontology for General Medical Science (OGMS). The Basic Formal Ontology (BFO) is an upper-level ontology for both the MF and the OGMS. The picture below is a view of the class hierarchy of the MD showing details of the class "Paranoid Schizophrenia" in the right pane of the windows of the beta release of Protege 5, an open source ontology development environment (click on the image to enlarge it).

The following is a tree view of the "Mental Disease Course" class (click on the image to enlarge it):

Ontology constructs defined by the OWL2 language can help establish common semantics (meaning) and relationships between entities across domains. These constructs provide automated inferencing capabilities such as equivalence (e.g., owl:sameAs and owl:equivalentClass) and subsumption (e.g., rdfs:subClassOf) relationships between entities.

In addition, publishing data sources following Linked Open Data (LOD) principles and semantic search using federated SPARQL queries can help answer new research questions. Another application is semantic annotation for natural language processing (NLP) applications.

 

Ontologies as knowledge representation formalism for clinical decision support (CDS)

As knowledge representation formalism, ontologies are well suited for modeling complex medical knowledge and can facilitate reasoning during the automated execution of clinical practice guidelines (CPGs) and Care Pathways (CPs) based on patient data at the point of care. Several approaches to modelling CPGs and CPs have been proposed in the past including PROforma, HELEN, EON, GLIF, PRODIGY, and SAGE. However, the lack of free and open source tooling has been a major impediment to a wide adoption of these knowledge representation formalisms. OWL has the advantage of being a widely implemented W3C Recommendation with available mature open source  tools.

In practice, the medical knowledge contained in CPGs can be manually translated into IF-THEN statements in most programming languages. Executable CDS rules (like other complex types of business rules) can be implemented with a production rule engine using forward chaining. This is the approach taken by OpenCDS and some large scale CDS implementations in real world healthcare delivery settings. This allows CDS software developers to externalize the medical knowledge contained in clinical 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.

However, production rule systems have a limitation. They do not scale because they require writing a rule for each clinical concept code (there are more than 311,000 active concepts in SNOMED CT alone). An alternative is to exploit the class hierarchy in an ontology so that subclasses of a given superclass can inherit the clinical rules that are applicable to the superclass (this is called subsumption). In addition to subsumption, an OWL ontology also support reasoning with description logic (DL) axioms [4].

An ontology designed for a clinical decision support (CDS) system can integrate the clinical rules from a CPG, a domain ontology like the Mental Disorder (MD) ontology, and the patient medical record from an EHR database in order to provide inferences in the form of treatment recommendations at the point of care. The OWL API [5] facilitates the integration of ontologies into software applications. It supports inferencing using reasoners like Pellet and HermiT. OWL2 reasoning capabilites can be enhanced with rules represented in SWRL (Semantic Web Rule Language) which is implemented by reasoners like Pellet as well as the Protege OWL development environement. In addition to inferencing, another benefit of an OWL-based approach is transparency: the CDS system can provide an explanation or justification of how it arrives at the treatment recommendations.

Nonetheless, these approaches are not mutually exclusive: a production rule system can be integrated with business processes, ontologies, and predictive analytics models. Predictive analytics models provide a probabilistic approach to treatment recommendations to assist in the clinical decision making process.

References

[1]  Janna Hastings, Werner Ceusters, Mark Jensen, Kevin Mulligan and Barry Smith. Representing mental functioning: Ontologies for mental health and disease. Proceedings of the Mental Functioning Ontologies workshop of ICBO 2012, Graz, Austria.

[2]  Ceusters, W. and Smith, B. (2010a). Foundations for a realist ontology of mental disease. Journal of Biomedical Semantics, 1(1), 10.

[3] Hastings, J., Smith, B., Ceusters, W., and Mulligan, K. (2012). The mental functioning ontology. http://code.google.com/p/mental-functioning-ontology/, last accessed August 24, 2014

[4] 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.

[5] Matthew Horridge, Sean Bechhofer. The OWL API: A Java API for OWL Ontologies Semantic Web Journal 2(1), Special Issue on Semantic Web Tools and Systems, pp. 11-21, 2011.

Natural Language Processing (NLP) for Clinical Decision Support: A Practical Approach

A significant portion of the electronic documentation of clinical care is captured in the form of unstructured narrative text like psychotherapy and progress notes. Despite the big push to adopt structured data entry (as required by the Meaningful Use incentive program for example), many clinicians still like to document care using free narrative text. The advantage of using narrative text as opposed to coded entries is that narrative text can tell the story of the patient and the care provided particularly in complex cases. My opinion is that free narrative text should be used to complement coded entries when necessary to capture relevant information.

Furthermore, medical knowledge is expanding very rapidly. For example, PubMed has more than 24 millions citations for biomedical literature from MEDLINE, life science journals, and online books. It is impossible for the human brain to keep up with that amount of knowledge. These unstructured sources of knowledge contain the scientific evidence that is required for effective clinical decision making in what is referred to as Evidence-Based Medicine (EBM).

In this blog, I discuss two practical applications of Natural Language Processing (NLP). The first is the use of NLP tools and techniques to automatically extract clinical concepts and other insight from clinical notes for the purpose of providing treatment recommendations in Clinical Decision Support (CDS) systems. The second is the use of text analytics techniques like clustering and summarization for Clinical Question Answering (CQA).

The emphasis of this post is on a practical approach using freely available and mature open source tools as opposed to an academic or theoretical approach. For a theoretical treatment of the subject, please refer to the book Speech and Language Processing by Daniel Jurafsky and James Martin.

Clinical NLP with Apache cTAKES

Based on the Apache Unstructured Information Management Architecture (UIMA) framework and the Apache OpenNLP natural language processing toolkit, Apache cTAKES provides a modular architecture utilizing both rule-based and machine learning techniques for information extraction from clinical notes. cTAKES can extract named entities (clinical concepts) from clinical notes in plain text or HL7 CDA format and map these entities to various dictionaries including the following Unified Medical Language System (UMLS) semantic types: diseases/disorders, signs/symptoms, anatomical sites, procedures, and medications.

cTAKES includes the following key components which can be assembled to create processing pipelines:

• Sentence boundary detector based on the OpenNLP Maximum Entropy (ME) sentence detector.
• Tokenizor
• Normalizer using the National Library of Medicine's Lexical Variant Generation (LVG) tool
• Part-of-speech (POS) tagger
• Shallow parser
• Named Entity Recognition (NER) annotator using dictionary look-up to UMLS concepts and semantic types. The Drug NER can extract drug entities and their attributes such as dosage, strength, route, etc.
• Assertion module which determines the subject of the statement (e.g., is the subject of the statement the patient or a parent of the patient) and whether a named entity or event is negated (e.g., does the presence of the word "depression" in the text implies that the patient has depression).

Apache cTAKES 3.2 has added YTEX, a set of extensions developed at Yale University which provide integration with MetaMap, semantic similarity, export to Machine Learning packages like Weka and R, and feature engineering.

The following diagram from the Apache cTAKES Wiki provides an overview of these components and their dependencies (click to enlarge):

Source: Apache cTAKES wiki

Massively Parallel Clinical Text Analytics in the Cloud with GATECloud

The General Architecture for Text Engineering (GATE) is a mature, comprehensive, and open source text analytics platform. GATE is a family of tools which includes:

• GATE Developer: an integrated development environment (IDE) for language processing components with a comprehensive set of available plugins called CREOLE (Collection of REusable Objects for Language Engineering). 
• GATE Embedded: an object library for embedding services developed with GATE Developer into third-party applications.
• GATE Teamware: a collaborative semantic annotation environment based on a workflow engine for creating manually annotated corpora for applying machine learning algorithms. 
• GATE Mímir: the "Multi-paradigm Information Management Index and Repository" which supports a multi-paradigm approach to index and search over text, ontologies, and semantic metadata.
• GATE Cloud: a massively parallel clinical text analytics platform (Platform as a Service or PaaS) built on the Amazon AWS Cloud.

What makes GATE particularly attractive is the recent addition of GATECloud.net PaaS which can boost the productivity of people involved in large scale text analytics tasks.

 

Clustering, Classification, Text Summarization, and Clinical Question Answering (CQA)

 

An unsupervised machine learning approach called Clustering can be used to classify large volumes of medical literature into groups (clusters) based on some similarity measure (such as the Euclidean distance). Clustering can be applied at the document, search result, and word/topic levels. Carrot2 and Apache Mahout are open source projects that provide several methods for document clustering. For example, the Latent Dirichlet Allocation learning algorithm in Apache Mahout automatically clusters words into topics and documents into mixtures of topics. Other clustering algorithms in Apache Mahout include: Canopy, Mean-Shift, Spectral, K-Means and Fuzzy K-Means. Apache Mahout is part of the Hadoop ecosystem and can therefore scale to very large volumes of unstructured text.

Document classification essentially consists in assigning predefined set of labels to documents. This can be achieved through supervised machine learning algorithms. Apache Mahout implements the Naive Bayes classifier.

Text summarization techniques can be used to present succinct and clinically relevant evidence to clinicians at the point of care. MEAD (http://www.summarization.com/mead/) is an open source project that implements multiple summarization algorithms. In the biomedical domain, SemRep is a program that extracts semantic predications (subject-relation-object triples) from biomedical free text. Subject and object arguments of each predication are concepts from the UMLS Metathesaurus and the relation is from the UMLS Semantic Network (e.g., TREATS, Co-OCCURS_WITH). The SemRep summarization provides a short summary of these concepts and their semantic relations.

AskHermes (Help clinicians to Extract and aRrticulate Multimedia information for answering clinical quEstionS) is a project that attempts to implement these techniques in the clinical domain. It allows clinicians to enter questions in natural language and uses the following unstructured information sources: MEDLINE abstracts, PubMed Central full-text articles, eMedicine documents, clinical guidelines, and Wikipedia articles.

The processing pipeline in AskHermes includes the following: Question Analysis, Related Questions Extraction, Information Retrieval, Summarization and Answer Presentation. AskHermes performs question classification using MMTx (MetaMap Technology Transfer) to map keywords to UMLS concepts and semantic types. Classification is achieved through supervised machine learning algorithms such as Support Vector Machine (SVM) and conditional random fields (CFRs). Summarization and answer presentation are based on clustering techniques. AskHermes is powered by open source components including: JBoss Seam, Weka, Mallet , Carrot2 , Lucene/Solr, and WordNet (a lexical database for the English language).

Enabling Scalable Realtime Healthcare Analytics with Apache Spark

Modern and massively parallel computing platforms can process humongous amounts of data in real time to obtain actionable insights for effective clinical decision making. In this blog, I discuss an emerging Big Data platform called Apache Spark and its application to remote real-time healthcare monitoring using data from medical devices and wearable sensors. The goal is to provide effective remote care for an increasingly aging population as well as public health surveillance.

The Apache Spark Framework

Apache Spark has emerged during the last couple of years as an innovative platform for Big Data and in-memory cluster computing capable of running programs up to 100x faster than traditional Hadoop MapReduce. Apache Spark is written in Scala, a functional programming language (see my previous post titled Navigating in Scala land). Spark also offers a Java and a Python APIs. The Scala API allows developers to interact with Spark by using very concise and expressive Scala code.

The Spark stack also includes the following integrated tools:

• Spark SQL which allows relational queries expressed in SQL, HiveQL, or Scala to be executed using Spark through a data abstraction called SchemaRDD. Supported data sources include Parquet files (a columnar storage format for Hadoop), JSON datasets, or data stored in Apache Hive.


• Spark Streaming which enables fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, Flume, Twitter, ZeroMQ or plain old TCP sockets. The ingested data can be directly processed with Spark built-in Machine Learning algorithms.


• MLlib (Machine Learning Library) provides a library of practical Machine Learning algorithms including support vector machines (SVM), logistic regression, decision trees, naive Bayes, and k-means clustering.


• GraphX which provides graph-parallel computation for graph-analytics application like social networks.

Apache Spark can also play nicely with other frameworks within the Hadoop ecosystem. For example, it can run standalone or on a Hadoop 2's YARN cluster manager, on Amazon EC2 or a Mesos cluster manager. Spark can also read data from HFDS, HBase, Cassandra or any other Hadoop data source. Other noteworthy integrations include:

• SparkR, an R package allowing the use of Spark from R, a very popular open source software environment for statistical computing with more that 5800 packages including Machine Learning packages; and


• H2O-Sparkling which provides an integration with the H2O platform through in-memory sharing with Tachyon, a memory-centric distributed file system for data sharing across cluster frameworks. This allows Spark applications to leverage advanced distributed Machine Learning algorithms supported by the H2O platform like emerging Deep Learning algorithms.

 

Wearable Sensors for Remote Healthcare Monitoring 

Three factors are contributing to the availability of massive amounts of clinical data: the rising adoption of EHRs by providers thanks in part to the Meaningful Use incentive program; the increasing use of medical devices including wearable sensors used by patients outside of healthcare facilities; and medical knowledge (for example in the form of medical research literature).

One promising area in Healthcare Informatics where Big Data architectures like the one provided by Apache Spark can make a difference is in applications using data from wearable health monitoring sensors for anomaly detection, care alerting, diagnosis, care planning, and prediction. For example, anomaly detection can be performed at scale using the k-means clustering machine learning algorithm in Spark.

These sensors and devices are part of a larger trend called the "Internet of Things". They enable new capabilities such as remote health monitoring for personalized medicine and chronic care management for an increasingly aging population as well as public health surveillance for outbreaks and epidemics.

Wearable sensors can collect vital signs data like weight, temperature, blood pressure (BP), heart rate (HR), blood glucose (BG), respiratory rate (RR), electrocardiogram (ECG), oxygen saturation (SpO2), and Photoplethysmography (PPG). Spark Streaming can be used to perform real-time stream processing on sensors data and the data can be processed and analyzed using the Machine Learning algorithms available in MLlib and the other integrated frameworks like R and H2O. What makes Spark particularly suitable for this type of applications is that sensor data meet the Big Data criteria of volume, velocity, and variety.

Researchers predict that internet use on mobile phones will increase 20-fold in Africa in the next five years. The number of mobile subscriptions in sub-Saharan Africa is expected to reach 635 millions by the end of this year. This unprecedented level of connectivity (fueled in part by the historical lack of land line infrastructure) provides opportunities for effective public health surveillance and disease management in the developing world.

Apache Spark is the type of open source computing infrastructure that is needed for distributed, scalable, and real-time healthcare analytics for reducing healthcare costs and improving outcomes.

Improving the quality of mental health and substance use treatment: how can Informatics help?

According to the 2012 National Survey on Drug Use and Health, an estimated 43.7 million adults aged 18 or older in the United States had mental illness in the past year. This represents 18.6 percent of all adults in this country. Among those 43.7 million adults, 19.2 percent (8.4 million adults) met criteria for a substance use disorder (i.e., illicit drug or alcohol dependence or abuse). In 2012, an estimated 9.0 million adults (3.9 percent) aged 18 or older had serious thoughts of suicide in the past year.

Mental health and substance use are often associated with other issues such as:

• Co-morbidity involving other chronic diseases like HIV, hepatitis, diabetes, and cardiovascular disease.


• Overdose and emergency care utilization.


• Social issues like incarceration, violence, homelessness, and unemployment.

It is now well established that addiction is a chronic disease of the brain and should be treated as such from a health and social policy standpoint.

The regulatory framework

• The Affordable Care Act (ACA) requires non-grandfathered health plans in the individual and small group markets to provide essential health benefits (EHBs) including mental health and substance use disorder benefits.  


• Starting in 2014, insurers can no longer deny coverage because of a pre-existing mental health condition.


• The ACA requires health plans to cover recommended evidence-based prevention and screening services including depression screening for adults and adolescents and behavioral assessments for children.


• On November 8, 2013, HHS and the Departments of Labor and Treasury released the final rules implementing the Paul Wellstone and Pete Domenici Mental Health Parity and Addiction Equity Act of 2008 (MHPAEA). 


• Not all behavioral health specialists are eligible to the Meaningful Use EHR Incentive program created by the Health Information Technology for Economic and Clinical Health Act (HITECH) of 2009.

 

Implementing Clinical Practice Guidelines (CPGs) with Clinical Decision Support (CDS) systems

 

Clinical Decision Support (CDS) can help address key challenges in mental health and substance use treatment such as:

• Shortages and high turnover in the addiction treatment workforce.


• Insufficient or lack of adequate clinician education in mental health and addiction medicine.


• Lack of implementation of available evidence-based clinical practice guideline (CPGs) in mental health and addiction medicine.

For example, there are a number of scientifically validated CPGs for the Medication Assisted Treatment (MAT) of opioid addiction using methadone or buprenorphine. These evidence-based CPGs can be translated into executable CDS rules using business rule engines. These executable clinical rules should also be seamlessly integrated with clinical workflows.

The complexity and costs inherent in capturing the medical knowledge in clinical guidelines and translating that knowledge into executable code remains an impediment to the widespread adoption of CDS software. Therefore, there is a need for standards that facilitate the sharing and interchange of CDS knowledge artifacts and executable clinical guidelines. The ONC Health eDecision Initiative has published specifications to support the interoperability of CDS knowledge artifacts and services.

Ontologies as knowledge representation formalism are well suited for modeling complex medical knowledge and can facilitate reasoning during the automated execution of clinical guidelines based on patient data at the point of care.

The typical Clinical Practice Guideline (CPG) is 50 to 150 pages long. Clinical Decision Support (CDS) should also include other forms of cognitive aid such as Electronic Checklists, Data Visualization, Order Sets, and Infobuttons.

The issues of human factors and usability of CDS systems as well as CDS integration with clinical workflows have been the subject of many research projects in healthcare informatics. The challenge is to be bring these research findings into the practice of developing clinical systems software.

Learning from Data

Learning what works and what does not work in clinical practice is important for building a learning health system. 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 example, CER and PCOR can help answer questions about the comparative effectiveness of pharmacological and  psychotherapeutic interventions in mental health and substance abuse treatment. This is a form of Practice-Based Evidence (PBE) that is necessary to close the evidence loop.

Three factors are contributing to the availability of massive amounts of clinical data: the rising adoption of EHRs by providers (thanks in part to the Meaningful Use incentive program), medical devices (including those used by patients outside of healthcare facilities), and medical knowledge (for example in the form of medical research literature). Massively parallel  computing platforms such as Apache Hadoop or Apache Spark can process humongous amounts of data (including in real time) to obtain actionable insights for effective clinical decision making.

The use of predictive modeling for personalized medicine (based on statistical computing and machine learning techniques) is becoming a common practice in healthcare delivery as well. These models can predict the health risk of patients (for pro-active care) based on their individual health profiles and can also help predict which treatments are more likely to lead to positive outcomes.

Embedding Visual Analytics capabilities into CDS systems can help clinicians obtain deep insight for effective understanding, reasoning, and decision making through the visual exploration of massive, complex, and often ambiguous data. For example, Visual Analytics can help in comparing different interventions and care pathways and their respective clinical outcomes for a patient or population of patients over a certain period of time through the vivid showing of causes, variables, comparisons, and explanations.

Genomics of Addiction and Personalized Medicine

Advances in genomics and pharmacogenomics are helping researchers understand treatment response variability among patients in addiction treatment. Clinical Decision Support (CDS) systems can also be used to provide cognitive support to clinicians in providing genetically guided treatment interventions.

Quality Measurement for Mental Health and Substance Use Treatment

An important implication of the shift from a fee-for-service to a value-based healthcare delivery model is that existing process measures and the regulatory requirements to report them are no longer sufficient.

Patient-reported outcomes (PROs) and patient-centered measures include essential metrics such as mortality, functional status, time to recovery, severity of side effects, and remission (depression remission at six and twelve months). These measures should take into account the values, goals, and wishes of the patient. Therefore patient-centered outcomes should also include the patient's own evaluation of the care received.

Another issue to be addressed is the lack of data elements in Electronic Medical Record (EMR) systems for capturing, reporting, and analyzing PROs. This is the key to accountability and quality improvement in mental health and substance use treatment.

Using Natural Language Processing (NLP) for the automated processing of clinical narratives

Electronic documentation in mental health and substance use treatment is often captured in the form of narrative text such as psychotherapy notes. Natural Language Processing (NLP) and machine learning tools and techniques (such as named entity recognition) can be used to extract clinical concepts and other insight from clinical notes.

Another area of interest is Clinical Question Answering (CQA) that would allow clinicians to ask questions in natural language and extract clinical 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. It is impossible for the human brain to keep up with that amount of knowledge.

Computer-Based Cognitive Behavioral Therapy (CCBT) and mHealth

According to a report published last year by the California HealthCare Foundation and titled The Online Couch: Mental Health Care on the Web:

"Computer-based cognitive behavioral therapy (CCBT) cost-effectively leverages the Internet for coaching patterns in self-driven or provider-assisted programs. Technological advances have enabled computer systems designed to replicate aspects of cognitive behavior therapy for a growing range of mental health issues".

An example of a successful nationwide adoption of CCBT is the online behavioral therapy site Beating the Blues in the United Kingdom which has been proven to help patients suffering from anxiety and mild to moderate depression. Beating the Blues has been recommended for use in the NHS by the National Institute for Health and Clinical Excellence (NICE).

In addition, there is growing evidence to support the efficacy of mobile health (mHealth) technologies for supporting patent engagement and activation in health behavior change (e.g., smoking cessation).

 

Technologies in support of a Collaborative Care Model

There is sufficient evidence to support the efficacy of the collaborative care model (CCM) in the treatment of chronic mental health and substance use conditions.The CCM is based on the following principles:

• Coordinated care involving a multi-disciplinary care team.


• Longitudinal care plan as the backbone of care coordination.


• Co-location of primary care and mental health and substance use specialists.


• Case management by a Care Manager. 

Implementing an effective collaborative care model will require a new breed of advanced clinical collaboration tools and capabilities such as:

• Conversations and knowledge sharing using tools like video conferencing for virtual two-way face-to-face communication between clinicians (see my previous post titled Health IT Innovations for Care Coordination).


• Clinical content management and case management tools.


• File sharing and syncing allowing the longitudinal care plan to be synchronized and shared among all members of the care team.


• Light-weight and simple clinical data exchange standards and protocols for content, transport, security, and privacy. 

 

Patient Consent and Privacy

Because of the stigma associated with mental health and substance use, it is important to give patients control over the sharing of their medical record. Patients consent should be obtained about what type information is shared, with whom, and for what purpose. The patient should also have access to an audit trail of all data exchange-related events. Current paper-based consent processes are inefficient and lack accountability. Web-based consent management applications facilitate the capture and automated enforcement of patient consent directives (see my previous post titled Patient privacy at web scale).

Health IT Innovations for Care Coordination

The Business Case

According to an article by Bodenheimer et al. published in the January/February 2009 issue of Health Affairs and titled Confronting The Growing Burden Of Chronic Disease: Can The U.S. Health Care Workforce Do The Job?:

In 2005, 133 million americans were living with at least one chronic condition. In 2020, this number is expected to grow to 157 million. In 2005, sixty-three million people had multiple chronic illnesses, and that number will reach eighty-one million in 2020. 

Patients with co-morbidities are typically treated by multiple clinicians working for different healthcare organizations. Care Coordination is necessary for the effective treatment of these patients and reducing costs. Effective Care Coordination can reduce the number of redundant tests and procedures, hospital admissions and readmissions, medical errors, and patient safety issues related to the lack of medication reconciliation. 

According to a paper by Dennison and Hugues published in the Journal of Cardiovascular Nursing and titled Progress in Prevention Imperative to Improve Care Transitions for Cardiovascular Patients, direct communication between the hospital and primary care setting occurred only 3 percent of the time. According to the same paper, at discharge, a summary was provided only 12 percent of the time, and this occurrence remained poor at 4 weeks post-discharge, with only 51 percent of practitioners providing a summary. The paper concluded that this standard affected quality of care in 25 percent of follow-up visits.

Health Information Exchanges (HIEs) and emerging delivery models like the Accountable Care Organization (ACO) and the Patient-Centered Medical Home (PCMH) were designed to promote care coordination. However, according to an article by Furukawa et al. published in the August 2013 issue of Health Affairs and titled Hospital Electronic Health Information Exchange Grew Substantially In 2008–12:

In 2012, 51 percent of hospitals exchanged clinical information with unaffiliated ambulatory care providers, but only 36 percent exchanged information with other hospitals outside the organization. . . . In 2012 more than half of hospitals exchanged laboratory results or radiology reports, but only about one-third of them exchanged clinical care summaries or medication lists with outside providers.                      

Furthermore, the financial sustainability of many HIEs remains an issue. According to another article by Adler-Milstein et al. published in the same issue of Health Affairs and titled Operational Health Information Exchanges Show Substantial Growth, But Long-Term Funding Remains A Concern, "74 percent of health information exchange efforts report struggling to develop a sustainable business model".  

There are other obstacles to care coordination including the existing fee-for-service healthcare delivery model (as opposed to a value-based model), the lack of interoperability between healthcare information systems, and the lack of adoption of effective collaboration tools.

According to a report by the Institute of Medicine (IOM) titled  The Healthcare Imperative: Lowering Costs and Improving Outcomes, a program designed to improve care coordination could result in national annual savings of $240.1 billions.

What Can We Learn From High Risk Operations in Other Industries?

Similar breakdowns in communication during shift handovers have also been observed in risky operating environments, sometimes with devastating consequences. In the aerospace industry, human factors research and training have played an important role in successfully addressing the issue. A research paper by Parke and Mishkin titled Best Practices in Shift Handover Communication: Mars Exploration Rover Surface Operations included the following recommendations:

• Two-way Communication, Preferably Face-to-Face. . . . Two-way communication enables the incoming worker to ask questions and rephrase the material to be handed over, so as to expose these differences [in mental model].



• Face-to-Face Handovers with Written Support. Face-to-face handovers are improved if they are supported by structured written material—e.g., a checklist of items to convey, and/or a position log to review. 



• Content of Handover Captures Intent. Handover communication works best if it captures problems, hypotheses, and intent, rather than simply lists what occurred.

While the logistics of healthcare delivery does not always permit physical face-to-face communication between clinicians during transitions of care, the web has seen an explosion in online collaboration tools. Innovative organizations have embraced these technologies giving rise to a new breed of enterprise software known as Enterprise 2.0 or Social Enterprise Software. This new breed of software is not only social, but also mobile, and cloud-based.

Care Coordination in the Health Enterprise 2.0

• Collaborative Authoring of a Longitudinal Care Plan. From a content perspective, the Care Plan is the backbone of Care Coordination. The Care Plan should be comprehensive and standardized (similar to the checklist in aerospace operations). It should include problems, medications, orders, results, care goals (taking into consideration the patient's wishes and values), care team members and their responsibilities, and actual patient outcomes (e.g., functional status). Clinical Decision Support (CDS) tools can be used to dynamically generate a basic Care Plan based on the patient's specific clinical data. This basic Care Plan can be used by members of the care team to build a more elaborate Longitudinal Care Plan. CDS tools can also automatically generate alerts and reminders for the care team.



• Communication and Collaboration using Enterprise 2.0 Software.  These tools should be used to enable collaboration between all members of the care team which include not only clinicians, but also non-clinician caregivers, and the patient herself. Beyond email, these tools allow conversations and knowledge sharing through instant messaging, video conferencing (for virtual two-way face-to-face communication), content management, file syncing (allowing the longitudinal care plan to be synchronized and shared among all members of the care team), search, and enterprise social networking (because clinical work is a social activity like most human activities). A providers directory should make it easy for users to find a specific provider and all their contact information based on search criteria such as location, specialty, knowledge, experience, and telephone number.



• Light Weight Standards and Protocols for Content, Transport, Security, and Privacy. The foundation standards are: REST, JSON, OAuth2, and OpenID Connect. An emerging approach that could really help put patients in control of the privacy of their electronic medical record is the OAuth2.0-based User-Managed Access (UMA) Protocol of the Kantara Initiative (see my previous post titled Patient Privacy at Web Scale). Initiatives like the ONC-sponsored RESTful Health Exchange (RHEX) project and the HL7 Fast Healthcare Interoperability Resources (FHIR) hold great promise.



• Case Management Tools. They are typically used by Nurse Practionners (Case Managers) in Medical Homes, a concept popularized by the Patient-Centered Medical Home healthcare delivery model to coordinate care. These tools integrate various capabilities such as risk stratification (using predictive modeling) to identify at-risk patients, content management (check-in, check-out, versioning), workflows (human tasks), communication, business rule engine, and case reporting/analytics capabilities.

Essential IT Capabilities Of An Accountable Care Organization (ACO)

The Certification Commission for Health Information Technology (CCHIT) recently published a document entitled A Health IT Framework for Accountable Care. The document identifies the following key processes and functions necessary to meet the objectives of an ACO:

• Care Coordination
• Cohort Management
• Patient and Caregiver Relationship Management
• Clinician Engagement
• Financial Management
• Reporting
• Knowledge Management.

The key to success is a shift to a data-driven healthcare delivery. The following is my assessment of the most critical IT capabilities for ACO success:

• Comprehensive and standardized care documentation in the form of electronic health records including as a minimum: patients' signs and symptoms, diagnostic tests, diagnoses, allergies, social and familiy history, medications, lab results, care plans, interventions, and actual outcomes. Disease-specific Documentation Templates can support the effective use of automated Clinical Decision Support (CDS). Comprehensive electronic documentation is the foundation of accountability and quality improvement.


• Care coordination through the secure electronic exchange and the collaborative authoring of the patient's medical record and care plan (this is referred to as clinical information reconciliation in the CCHIT Framework). This also requires health IT interoperability standards that are easy to use and designed following rigorous and well-defined software engineering practices. Unfortunately, this has not always been the case, resulting in standards that are actually obstacles to interoperability as opposed to enablers of interoperability. Case Management tools used by Medical Homes (a concept popularized by the Patient-Centered Medical Home model) can greatly facilitate collaboration and Care Coordination.


• Patients' access to and ownership of their electronic health records including the ability to edit, correct, and update their records. Patient portals can be used to increase patients' health literacy with health education resources. Decision aids comparing the benefits and harms of various interventions (Comparative Effectiveness Research) should be available to patients. Patients' health behavior change remains one of the greatest challenges in Healthcare Transformation. mHealth tools have demonstrated their ability to support Patient Activation.


• Secure communication between patients and their providers. Patients should have the ability to specify with whom, for what purpose, and the kind of medical information they want to share. Patients should have access to an audit trail of all access events to their medical records just as consumers of financial services can obtain their credit record and determine who has inquired about their credit score.


• Clinical Decision Support (CDS) as well as other forms of cognitive aids such as Electronic Checklists, Data Visualization, Order Sets, Infobuttons, and more advanced Clinical Question Answering (CQA) capabilities (see my previous post entitled Automated Clinical Question Answering: The Next Frontier in Healthcare Informatics). The unaided mind (as Dr. Lawrence Weed, the father of the Problem-Oriented Medical Record calls it) is no longer able to cope with the large amounts of data and knowledge required in clinical decision making today. CDS should be used to implement clinical practice guidelines (CPGs) and other forms of Evidence-Based Medicine (EBM).
  
  However, the delivery of care should also take into account the unique clinical characteristics of individual patients (e.g., co-morbidities and social history) as well as their preferences, wishes, and values. Standardized Clinical Assessment And Management Plans (SCAMPs) promote care standardization while taking into account patient preferences and the professional judgment of the clinician. CDS should be well integrated with clinical workflows (see my my previous post entitled Addressing Challenges to the Adoption of Clinical Decision Support (CDS) Systems).


• Predictive risk modeling to identity at-risk populations and provide them with pro-active care including early screening and prevention. For example, predictive risk modeling can help identify patients at risk of hospital re-admission, an important ACO quality measure.


• Outcomes measurement with an emphasis on patient outcomes in addition to existing process measures. Examples of patient outcome measures include: mortality, functional status, and time to recovery.


• Clinical Knowledge Management (CKM) to disseminate knowledge throughout the system in order to support a learning health system. The Institute of Medicine (IOM) released a report titled  Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care. The report describes the learning health system as:
  
  

  "delivery of best practice guidance at the point of choice, continuous learning and feedback in both health and health care, and seamless, ongoing communication among participants, all facilitated through the application of IT."



• Applications of Human Factors research to enable the effective use of technology in clinical settings. Examples include: implementation of usability guidelines to reduce Alert Fatigue in Clinical Decision Support (CDS), Checklists, and Visual Analytics. There are many lessons to be learned from other mission-critical industries that have adopted automation. Following several incidents and accidents related to the introduction of the Glass Cockpit about 25 years ago, Human Factors training known as Cockpit Resource Management (CRM) is now standard practice in the aviation industry.

Addressing Challenges to the Adoption of Clinical Decision Support (CDS) Systems: A Practical Approach

Laptop and stethoscope by jfcherry is licensed under CC BY-SA 2.0

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):

Proposed service-oriented architecture (SOA) for whole genome sequence (WGS)-enabled CDS by Brandon M. Welch, Salvador Rodriguez Loya, Karen Eilbeck, and Kensaku Kawamoto is licensed under CC BY 3.0

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.

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Statistical Computing and Machine Learning with R

The use of predictive risk models for personalized medicine is becoming a common practice in healthcare delivery. These models can predict the health risk of patients based on their individual health profiles. Examples include models for predicting breast cancer, stroke, cardiovascular disease, Alzheimer's disease, chronic kidney disease, diabetes, hypertension, and operative mortality for patients undergoing cardiac surgery. These predictive models are created through data analysis using statistical computing.

Predictive risk modeling can be used to identity at-risk populations and provide them with pro-active care including early screening and prevention. For example, predictive risk modeling can help identify patients at risk of hospital re-admission, an important Accountable Care Organization (ACO) quality measure.

Another important challenge in healthcare is to discover what works and what does not work in clinical practice. Comparative Effectiveness Research (CER), an emerging trend in Evidence Based Practice (EBP), has been defined by the Federal Coordinating Council for CER as "the conduct and synthesis of research comparing the benefits and harms of different interventions and strategies to prevent, diagnose, treat, and monitor health conditions in 'real world' settings."

Despite their inherent methodological challenges (lack of randomization leading to possible bias and confounding), observational studies (using real world clinical data) are increasingly recognized as complementary to Randomized Control Trials (RCTs) and an important tool in clinical decision making and health policy.

Statistical Computing and Machine Learning are essential components of intelligent health IT systems. Over the last few years, the free and open source R Project for Statistical Computing has emerged as one the most popular tools for data analysis. This poll by kdnuggets.com shows the breakdown in popularity of various data mining and analytic tools.

R supports several Machine Learning algorithms including:

• Nearest Neighbor
• Naive Bayes
• Decision Trees
• Logistic Regression
• Neural Networks
• Support Vector Machines
• Association Rules
• k-Means Clustering

A technique called "Ensemble Methods" which consists in combining multiple models into one can be used to achieve a higher level of accuracy than its components. There are also R packages for niche methods like the Latent Class Causal Analysis (LCCA) Package for R. LCA is used in behavioral health research.

The following are very useful resources for doing statistical computing and data mining with R:
 

• RStudio: an Integrated Development Environment (IDE) for R


• ggplot2: statistical graphics and plotting system for R


• sqldf: a package for manipulating R data frames using SQL


• RMySQL: R interface to the MySQL database


• RMongo: MongoDB Database interface for R
  


• RHIPE: Big Data analysis using R and Hadoop. RHIPE stands for R and Hadoop Integrated Programming Environment. This approach is referred to as D&R (Divide and Recombine) Analysis of Large Complex Data (see this tech report on D&R from the RHIPE team)


• RHadoop:  Big Data analysis using R and Hadoop. This tool provides Hadoop MapReduce functionality in R


• Rattle: A Graphical User Interface for Data Mining using R. This tool can export predictive models in Predictive Model Markup Language (PMML) format.