The Next Generation Electronic Medical Record

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Kyle Silvestro provides his insight on the next generation electronic medical record as most EMR’s have not been designed as clinical systems.

A fundamental shift is rapidly taking place away from the traditional flow of information as defined by payers, physicians, hospital systems, and suppliers. It is clear that we are at the start of a major shift in the demand from consumers and providers for better information and easier ways to share experiences. All stakeholders in healthcare must immediately begin to confront the decision of how to interact with these new technologies and networks, and potentially adopt and integrate them into their business and technology strategies.

Knowledge becomes Guidance

Traditionally science gives knowledge to clinicians, who in turn pass on this knowledge to medical patients. However, in a modern healthcare setting providers and patients need assistance in the acquisition of knowledge relevant to their conditions, treatment and the opportunity to discuss this knowledge with their peers, friends, family, advocates and other clinicians.

The problem is not a lack of will or incentive. Cutting through this Gordian Knot involves getting enough information from the physician, nurse and patients to ensure the proper outcome. Clinical databases are scarce in healthcare today and most analytical tools have trouble extracting meaningful information from free text, claims and pharmacy data.  Most EMR’s have not been designed as clinical systems and have trouble sending or accepting 3rd party clinical data.  Every day the US generates millions of dictated and typed medical reports each requiring a laborious and time-consuming process of data entry and manipulation, where highly trained and expensive experts manually cull the needed information from each report. Many HIT vendors can’t support the granular clinical data culled out of these reports to meet meaningful use and other regulatory requirements put in place by the federal government.

With this guidance in mind clinicians now use a combination of data capture methods and devices along with natural language processing, semantic search and knowledge driven software to solve the myriad of logistical, technological, financial and administrative challenges inherent in the clinical documentation and care giving process. The Health Information Technology for Economic and Clinical Health (HITECH) Act brings together, for the first time, a focus on clinical documentation standards (CDA), interoperability of healthcare data (SNOMED, ICD-9, ICD-10), EHR adoption, portability of data (HIE, RHIO), and physicians incentives designed to speedily and accurately match disparate patients information when and where it’s needed, securely.   NLP allows clinicians to document care any way they chose (pen, keyboard, and dictation) by providing a solution that can identify, encode, and extract a meaningful set of appropriate healthcare data into a certified EMR without changing their workflow. This unique solution combines front end data capture, NLP and semantic search supported by a semantic knowledge base and a next generation EMR / XML content Server / semantic index required to ascertain this goal.

Natural Language Processing

The information within a clinical note is not solely contained within fixed highly structured fields we normally associate with a database – such as name, address, phone number, etc. The description of the chronic problems, current medications, plan, HPI, and the specific Past Medical History, Review of Systems, Vitals, Labs, Social/Family History, Assessment, and plan are often expressed in free text narratives that require a skilled clinician or abstractor to interpret. This facet of clinical documentation makes up the “black art” of deciphering the active diagnosis against which to match a patient’s condition or status.

Natural language Processing is a multi-step process, starting with the lexical analysis of free text into its grammatical components: nouns, verbs, adjectives, etc. and its syntactic architecture of phrases, sentences, tables etc. In order to move beyond the specific language based elements of vocabulary, grammar and syntax one needs to “understand” meaning. Meaning is expressed in terms of concepts and relationships.

In order to extract meaning from “free text” one needs to associate subjects, verbs and objects with previously defined concepts. For example, “amoxicillin is an antibiotic” and by storing is concept of “what amoxicillin is” in a computer one can “interpret” the text containing these words (amoxicillin, antibiotic…). The most common relationship is an “is a” relationship, that is pharyngitis “is a” disease, tamoxifen “is a” estrogen antagonist and so on. Similarly one can build a list of diseases such adenosarcoma, small cell carcinoma, etc. or a hierarchy or taxonomy of diseases.

By parsing, the text for words in the taxonomy the computer can deduce that the word is a disease and then by utilizing the syntax, decide if a relationship exists. By classifying the word as either the subject or object of a phrase, the computer can extract a concept. Finally, by adding synonyms to the taxonomy the computer can greatly enhance its extraction of meaning from free text because it understands more words that are similar to those for which it “knows” a concept.

Once all the clinically relevant data has been normalized and tagged it is housed in a XML content (/ next gen EMR / Semantic Index) database the process of interpreting and analyzing the data it contains can begin.  Once encoded, the information is easily available and accessible for further clinical processes like billing, reimbursement, quality assurance analytics, pharmacovigilance, clinical trial identification, registry population, adverse event Identification, suspicious findings, and data mining.

Semantic Search

A natural language query based on a semantic output enables either patients, physicians or administrators answers to natural language questions to identify a select body of data. Patient medical information in the case of the patient is used to screen and/or match patients to trials. For meaningful use, core measures, PQRI, infection control, registry population. Finally, by extracting meaning from the clinical narrative data the VA will be able to categorize the clinical data parameters into a canonical form that may be used for matching or screening of patients with appropriate conditions. The power, specificity, and flexibility of semantic search allows for the discovery and dissemination of clinical information for optimal decision making.  Semantic search can help users:

  • Unlock unstructured clinical data for real time coding and extraction of information
  • Uncover trends and patterns to reach organizational goals
  • Anticipate business changes and needs
  • Reduce operational risk by anticipating clinical events
  • Identify candidates for clinical trials
  • Develop early warning systems for infection control across all facilities or geographic regions
  • Federate searches across all systems, file directories, journals & web sites across all data types

The description, storage and classification of patient information are a fundamental component of the clinical documentation process. Patient information must be gathered from multiple sources (the patient interview, the medical history, admission report and radiology reports) and be placed in a normalized or “canonical” format so that a match against meaningful use criteria and passed into an EMR.

Semantic Knowledge Base

An adult human rarely has to think hard in order to realize that a dog is an animal, which is a living thing, which is a material biological entity that therefore can eat, move, and procreate. In order to “understand” a document, first one has to map out concepts into a knowledge base or ontology. Quite simply an ontology is the description of knowledge about a certain domain (e.g. medicine, genomics, and clinical trials) as expressed in precisely defined terms that are interconnected through their relationships. The implications of such knowledge are easily available to the human mind but computers, as fast and powerful as they are, have no knowledge of this sort and can make no such inferences. However, we must rely on computers to do much of our searching and thinking and we must, therefore, know how to get computers to understand what we want. This in part means getting computers to understand and act on what we know about our world.

In the field of medicine, these efforts have been sponsored by the NIH for over 20 years and medical ontology’s and taxonomies are many and varied. There are many tools for managing ontological databases – both commercial and open source.

XML based Semantic Index or XML Content Server is the foundation for a Next Generation EMR

A sematic index (XML content server) is a platform that provides a set of services used to build applications and support business processes based on content. An XML content server‘s native data format is XML. XML content is accepted in “as is” form. Content in other formats is converted to an XML representation when loaded into the server. An XML content server manages its own content repository and is accessed using the W3C–standard XQuery language or semantic search engine. (By analogy, a relational database is a specialized server that manages its own repository and is accessed through SQL.) An XML content server should do much more than just store documents. It must be a secure, infinitely extensible platform for building content–driven applications. It needs to be flexible, granular, portable and modular in nature.  To support secondary data use it should have the ability to redact identifiable information while creating a longitudinal record.  Foster a semi open architecture allowing for easy collaboration with other solutions vendors that can add value with no or little integration cost to the end user.  Smart problem lists driven via dictation, adverse event identification, med reconciliation, etc.

Kyle Silvestro is the founder & CEO of Sytrue, a business and clinical intelligence platform that seamlessly integrate and structure disparate information to produce a truly longitudinal and comprehensive view of the patient population. 



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