Bringing the technology of FedEx parcel tracking to the Electronic Health Record (EHR) 1/2 0

What is tracking technology? The use of unique IDs (e.g. bar codes) in order to track items as they move through a system. ◦ Logistics ◦ Manufacturing ◦ Transportation of merchandise ◦ Handling of evidence in a criminal investigation ◦ Etc. 2/2 0

What does tracking technology do for the businesses who use it? FEDEX ◦ Coordination: With parcel tracking FEDEX links different activities to the parcel itself as it moves through the system. FEDEX activities: ◦ Receiving ◦ Addressing ◦ Billing ◦ Scheduling ◦ Delivering ◦ Locating (in transit) ◦ Relocating (if lost) ◦ Restituton (in case of loss) 3/2 0

Tracking technology Allows the customer (sender and recipient) ◦ to inform himself of the process of a parcel through the system ◦ to influence this process in case of need Tracking allows coordination of many different tasks without mixing up information about one parcel and another. 4/2 0

Tracking technology is already used as a matter of course for bank and credit card transactions cargo and logistics conveyor belt manufacturing mp3 files (for Digital Rights Management) archeological specimens forensic evidence tracking 5/2 0

Why should medicine adopt it? Patients ◦ grow older or change domicile ◦ change health care provider ◦ need specialist care. Because there is no unified ID system in the various health care institutions, all kinds of opportunities to ensure continuity of care are lost. Even where a reliable system of patient IDs exists, there is no ID system for ◦ the patient’s disorders ◦ the patient’s documents ◦ the many other items relevant to patient health, recorded always in a general way 6/2 0

Two levels to this problem the level of what is general ◦ the patient record systems used by different healthcare providers use different terms for the same general kinds of entities on the side of the patient (here ontology comes in) the level of what is particular ◦ tracking technology is not employed to link the different patient record systems together 7/2 0

Real world example (true story) Person P with complaint X ◦ Went out of town and became sick. ◦ Attended a series of different healthcare providers ◦ Informed each in turn that he had a quite specific ailment which needed urgently a quite specific kind of treatment. ◦ In part, at least, because the tracking mentality is so alien to the medical world, each new set of healthcare providers insisted on re-diagnosing from scratch. ◦ The process wasted time and almost killed him. 8/2 0

The Electronic Health Record Currently the Electronic Health Record is standardly conceived as a digitalized record of what the doctor thought, saw or did ◦ what tests he did ◦ what medicines he prescribed ◦ what hypotheses he formulated In the US, the record is primarily used to support hospital billing needs In other countries it is used as an aid to diagnosis 9/2 0

Currently the Electronic Health Record is focused on the doctor Data is organized according to which doctors saw the patient, not according to the problems which were treated, creating data clumps which are very often isolated from each other 10/ 20

This blocks continuity of care Different hospitals used different record systems If, in contrast, you have a health record which ◦ tracks the patient, ◦ and the patients different problems, ◦ and treatments through time, then you can ◦ glue together the records of the patient at different times ◦ create an easily accessible and re-usable history. 11/ 20

The Electronic Health Record Still works with general terms for almost all the entities recorded 12/ 20

The story of Jane Smith 13/ 20

Jane’s favourite supermarket July 4th, 1990 Jane goes shopping 14/ 20 The freezer section of Jane’s favourite supermarket The only available warning sign used outside An injured upper leg

A visit to the hospital 15/ 20

Diagnosis: severe spiral fracture of the femur 16/ 20

General types and particular instances General type ◦ Fracture ◦ Headache ◦ human being ◦ Death ◦ fall ◦ accident Particular instances ◦ Mary’s fracture ◦ my current headache ◦ Me ◦ Reagan’s death ◦ Mary’s fall ◦ Jim’s accident last Wednesday 17/ 20

Kinds of codes in the EHR The EHR uses specific codes (alphanumeric ID’s) for: ◦ Patients (for example your Social Security Number) ◦ Physicians (standardly assigned by the hospital) ◦ Times (date, time of day) ◦ Places (each hospital will use a different ID system e.g. for buildings) But it uses general ‘observation codes’ (‘obscodes’) for everything else. The obscodes are taken from standardized clinical terminologies The record tells us that there is some particular fracture – an instance of the general class ‘fracture’ – but which one? 18/ 20

Examples of observation codes used in the EHR /07/ closed fracture of shaft of femur /07/ Fracture, closed, spiral /07/ closed fracture of shaft of femur /07/ Accident in public building (supermarket) /07/ Essential hypertension /12/ benign polyp of biliary tract /03/ closed fracture of shaft of femur /03/ Accident in public building (supermarket) /04/ Other lesion on other specified region /05/ Essential hypertension 29822/08/ Closed fracture of radial head 29822/08/ Accident in public building (supermarket) /04/ closed fracture of shaft of femur /04/ Essential hypertension PtIDDateObsCodeNarrative /12/ malignant polyp of biliary tract PtID = Patient ID ObsCode = Observation Code taken from SNOMED (Systematized Nomenclature of Medicine)

557204/07/ closed fracture of shaft of femur /07/ Fracture, closed, spiral /07/ closed fracture of shaft of femur /07/ Accident in public building (supermarket) /07/ Essential hypertension /12/ benign polyp of biliary tract /03/ closed fracture of shaft of femur /03/ Accident in public building (supermarket) /04/ Other lesion on other specified region /05/ Essential hypertension 29822/08/ Closed fracture of radial head 29822/08/ Accident in public building (supermarket) /04/ closed fracture of shaft of femur /04/ Essential hypertension PtIDDateObsCodeNarrative /12/ malignant polyp of biliary tract Different patients, same fracture codes: Same (numerically identical) fracture ? Different patients, same fracture codes: Same (numerically identical) fracture ? Same patient, different dates, same fracture codes: same (numerically identical) fracture ? Same patient, same date, 2 different fracture codes: same (numerically identical) fracture ? Same patient, same date, 2 different fracture codes: same (numerically identical) fracture ? Problems 20/ 20 Different patients. Same supermarket? Same freezer section? Same patient, different dates, Different codes. Same (numerically identical) polyp ? Same patient, different dates, Different codes. Same (numerically identical) polyp ?

Enormous problems with current practices It is difficult to count the number of (numerically) different diseases, disorders, medical problems in a given patient This produces bad statistics on: ◦ Incidence ◦ Prevalence ◦ Cost It is difficult to identify outcomes (and thus determine value added) ◦ Is this the same disorder now as that which was recorded 3 years ago? 21/ 20

Enormous problems with current practices Suppose the doctor sees several patients all of whom complain of nasty viral warts. He sees in all their records that they all ‘visited swimming pool’ ◦ but the records do not tell him that they all visited the same swimming pool ◦ swimming pools are not identified via unique IDs. 22/ 20

unique numerical IDs for all concrete individual entities relevant to the diagnosis and therapy of each patient 23/ 24 The solution

This proposal can be implemented harmlessly The Fedex-style tracking layer would in 99% of the cases work behind the scenes ◦ would not force everybody to speak a new language ◦ or to learn new technology. Only in very rare cases would it require intervention from the doctor/coder ◦ but these are precisely the cases where the referent tracking data is of most help to both the doctor and the patient 24/ 20

It can be implemented incrementally For example, test it first ◦ in the hospital’s blood bank services ◦ in the hospital’s organ transplant services 25/ 20

In medicine The treatment of general types (diabetes, fracture, death) is dealt with quite successfully by medical terminologies and ontologies The treatment of particular instances is still underdeveloped 26/ 20

Solution: Referent Tracking Method: introduce Unique Identifiers for each relevant particular ◦ for Mary’s fracture ◦ John’s heart ◦ Jim’s tumor Result: An ever growing map of clinical cases, and of their interrelations to other clinical cases 27/ 20

Services provided by our solution ◦ automatic generation of IDs for particulars ◦ automatic assignment of these IDs to the particulars which need to be referred to in the EHR ◦ repository for all the Ids ◦ Database of statements relating the corresponding particulars to the types recorded in the EHR 28/ 20

Advantages of the solution Referent tracking can solve a number of problems in an elegant way Existing coding systems and technologies can be used for the implementation The IDs are generated by software behind the scenes, so that normal coding practices can continue as before 29/ 20

Advantages of the solution Because the same patient often attends different hospitals using different coding systems the use of common ID repositories will gradually lead to automatic mappings between terminologies Thus, the big problem of continuity of care in a world where different hospitals have different EHRs and thus use different terminologies will be resolved. 30/ 20

As IDs come to be associated with a plurality of codes from different coding systems  We can run statistical tests to find outliers ◦ codes which were misapplied ◦ or ill-defined Or we can test and enhance rules for reasoning postulated by coding systems such as SNOMED-CT 31/ 20 Advantages of the solution

Over time, the ID repository will come to serve as a benchmark of correctness for coding systems, allowing automatic step-by- step improvements and evidence-based integration 32/ 20 Advantages of the solution

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking System Components Referent Tracking Software Manipulation of statements about facts and beliefs Referent Tracking Datastore: IUI repository A collection of globally unique singular identifiers denoting particulars Referent Tracking Database A collection of facts and beliefs about the particulars denoted in the IUI repository Manzoor S, Ceusters W, Rudnicki R. Implementation of a Referent Tracking System. International Journal of Healthcare Information Systems and Informatics 2007;2(4):41-58.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Elementary RTS tuple types Relationships between particulars taken from a realism-based relation ontology Instantiation of a universal Annotation using terms from a non- realist terminology ‘Negative findings’ such as absences, missing parts, preventions, … Names for a particular

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Formalism includes management of changes 1.changes in the underlying reality: Particulars come into being, change and die 2.reassessments of what is considered to be relevant for inclusion (usefulness) 3.encoding of mistakes introduced during data entry or ontology development (who, when …) 4.changes in our knowledge of this reality Abdul Abdullah never existed

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Formalism includes management of changes 1.changes in the underlying reality: Particulars come into being, change and die 2.reassessments of what is considered to be relevant for inclusion (usefulness) 3.encoding of mistakes introduced during data entry or ontology development (who, when …) 4.changes in our knowledge of this reality ‘Jose Enriques’ never had a referent

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Advantages of Referent Tracking Preserves identity Allows to assert relationships amongst entities that are not generically true Appropriate representation of the time when relationships hold Deals with conflicting representations by keeping track of sources Allows for corrections without distorting what was originally believed Fully compatible with semantic web technologies

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Need for change and belief management Distinct sensors may hold different beliefs about whether a specific incident (e.g. # ) –really happened –is of a specific sort –counts as a medical error depending on what definition or rules they apply. They may differ in beliefs about –what caused the incident –how to prevent future happenings of incidents of the same sort. They may change their beliefs over time.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U XML Message ucore-message ESS Army Net-Centric Data Strategy Center of Excellence …

New York State Center of Excellence in Bioinformatics & Life Sciences R T U ucore- message ESS Army Net-Centric Data Strategy Center of Excellence

New York State Center of Excellence in Bioinformatics & Life Sciences R T U UCORE XML Message (3) 4th Brigade Represents a Readiness Report for a military unit. 1

New York State Center of Excellence in Bioinformatics & Life Sciences R T U

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Applying RT to UCore Messages RTS Middleware Reasoner Rules Ontology reads XML Message Communicate with RTS to assign IUI to entity referred to in XML message UCORE Messages

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Objectives Parsing XML messages –Analyze representations of message content in RTS what sorts of entities are involved? what are the relationships found between these entities? which ontology types are instantiated? –Validation of XML messages on ontological grounds –Reasoning with XML message content

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Message Parsing into Triples (Step 1) Iterates over the XML message through a Depth First strategy –Treats each XML element as a relation between possible entities –In Step 1 middleware does not yet use any knowledge of ontology or RTS rts:1002 ulex:PublishMessage rts:1003 rts:1003 DataSubmitterMetadata rts:1006 rts:1006 SystemIdentifier “ESS” rts:1006 SystemContact rts:1007 rts:1007 Organization rts:1008 rts:1008 name “Army Net- Centric … ESS Army Net-Centric Data Strategy Center of Excellence

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Step 1: XML Transformation into Triples Visualization

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Step 2: Triples Transformation By Rules Use rules to add or remove triples A rule based on triples divided into parts: –Head: Transformation Pattern –Body: Search pattern e.g.: ?x ulex:PublishMessage ?y -> ?x ro:instanceof uc:Document If two putative entities are linked by the ulex:PublishMessage element, then the first is of type UCore:document

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Output after the execution of step 2

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Output after the execution of step 2

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Output after the execution of step 2

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Tracking of Entities (Step 3) Resolve whether an entity is already assigned an IUI or not. Suppose that the middleware receives a second message. The message refers to the 4th Brigade. So during the execution of this step, reference to this military unit will be effected through IUI #1011, which was already registered for it in the RTS.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Message 2: After the processing of three steps

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reasoning validation In the second message, the supply level (#2019) of unit #1011’s stock of equipment #9001 is ‘2’ Implemented rule: if the supply level for this type of equipment is less then 3, then generate an alert to the effect that the troops are not ready for the mission: (?x uct:hasEquipmentSupplies ?y) (?z uct:equipmentSuppliesLevelOf ?y) (?z readiness.reporting:EquipmentSuppliesResourceAreaLevel ?l) lessThan(?l, 3) -> print(“The unit ”, ?x, “ is not ready for mission”)

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conclusion: Generalizability The approach can be used to reason with messages in single ontology formats but also to integrate messages with different formats, such as SNOMED, ICD, HL7 Goal: to create fully automatized interoperability corridors between existing silos of legacy data