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Prepared by Joanne Chicco and Graham Pegler Demand and Performance Evaluation Branch October 2010

SNOMED CT pioneers The NSW experience. Prepared by Joanne Chicco and Graham Pegler Demand and Performance Evaluation Branch October 2010. History of ED clinical data collection in NSW. Originally paper-based clinical information collected at a local level

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Prepared by Joanne Chicco and Graham Pegler Demand and Performance Evaluation Branch October 2010

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  1. SNOMED CT pioneers The NSW experience Prepared by Joanne Chicco and Graham PeglerDemand and Performance Evaluation Branch October 2010

  2. History of ED clinical data collection in NSW • Originally paper-based clinical information collected at a local level • Emergency Department Information System (EDIS) introduced in the 1980s contained a list of “doctor friendly terms” that were converted into ICD-9-CM codes and eventually ICD-10-AM codes • Collection of diagnosis was not mandatory • Some hospitals reported diagnosis very reliably, others hardly at all

  3. Electronic Medical Record (EMR) • Statewide rollout began in 2006 • Cerner FirstNet used as the Emergency Department module • Pioneer hospitals in Sydney South West Area Health Service - Royal Prince Alfred Hospital and Concord Hospital • Followed by Sydney West AHS, South Eastern Sydney Illawarra AHS, North Coast AHS, Greater Western AHS • Currently 41 hospitals using Cerner FirstNet and reporting clinical data in SNOMED CT. • Diagnosis is mandatory.

  4. Standardised Nomenclature Of MEDicine - Clinical Terms • Clinical terminologies were developed for use in electronic health records and computer assisted decision support and information management systems. • The purpose of clinical terminology is to support clinical software. • Terminologies enable the “computability” of clinical information. • Clinical terminologies such as SNOMED CT are “input” systems and codify the clinical information captured in an electronic health record during the course of patient care.

  5. Why choose SNOMED CT to report clinical data? • DOHA and NCCH were developing a National Emergency Department Diagnosis / Presenting Problem termset • NSW Health suggested the use of SNOMED CT terms as they would eventually be able to be recognised and used within an Electronic Medical Record and an Electronic Health Record • Clinicians prefer to choose diagnoses and presenting problems based on the language they use rather than the language of the International Classification of Disease

  6. Initial results • Clinicians were not restricted to a certain subset (or Reference Set) of terms within SNOMED CT (SCT) • They were able to choose a diagnosis or presenting problem from the full set of more than 300,000 terms • Many of the terms within SCT are not directly related to patient care (e.g. non-human subset, substances such as food) and there was some initial pushback from clinicians about the relevance of the list they were choosing from • There are many ways to describe the one condition - e.g. 85 ways to describe a heart attack

  7. Initial reactions • Clinicians chose to describe diagnoses in many different ways. • Low back pain and lumbago were the two diagnosis choices for this condition in EDIS, but in SCT low back pain was described as many as 18 ways within one Emergency Department in one month • How is the ED Director supposed to use this data that looks so different to manage his or her department? • How are the downstream data users supposed to report on this data that looks so different?

  8. NSW ED Termset creation • The immediate solution was to restrict diagnosis choice to a subset of Clinical Findings (as our Fundamental Reference Set) • Discovered the most frequently used terms used for diagnoses and reason for presentation • Debated with Advisory Group of clinicians and data managers • Devised the NSW Emergency Department Termset of approximately 1800 terms • Terms consist of diagnoses and reasons for encounter

  9. Termset creation – what did we discover? • Many terms used by Australian clinicians were not in SNOMED CT. For example: • Burn of airway • Unsettled baby • Closed head injury • Injury to cruciate ligament of knee • Subacute bowel obstruction • Jellyfish sting

  10. What did we do? • NEHTA’s National Clinical Terminology Information Service (NCTIS) were our conduit to the IHTSDO • NSW presented a brief business case to NEHTA as to why we required the terms to be included into SCT • NEHTA did further research, and lobbied on our behalf to have our terms included • Fast turnaround time – submitted to IHTSDO in September 2009 and 78% of our submissions were accepted and included in the SCT International Release in January 2010

  11. Emergency Department Reference Set (EDRS) • Developed by DOHA, NCCH, NEHTA and other stakeholders • This Reference Set was being developed at the same time as the NSW ED Termset • Consists of approximately 8000 terms from SNOMED CT • Collaboration between NSW Health and NEHTA has seen a 90% correlation of content between the two reference sets • EDRS is about to be trialled at 3 hospitals in Australia

  12. What did we learn? • The EMR User Interface is extremely important when choosing diagnoses • Use a “contains” search rather than a “starts with” search • Restrict the terms to be chosen – but not so much that it restricts the natural language choice by the clinicians • Restricting choice through reference sets, while great for secondary use, has implications for clinical use e.g. discharge summaries

  13. What do we need to think about? • Why are we using a clinical terminology if we are going to roll up the terms and use it as a classification? • We risk losing the richness of clinical data by rolling up to a subset of terms • What diagnosis will be allocated to the patient if that patient’s reason for presentation is not on the EDRS list? Will the diagnosis remain blank, will there be an “other” category to choose from or will “rolled up” categories exist? • How can we change the mindset of the current data collectors and users – SCT looks VERY different! • Reference sets still require analysis to ensure consistent categorisation of terms within the set itself

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