SNOMED CT Search & Data Entry Fadi El-Turk Anne Randorff Højen

Who are we? Anne Randorff Fadi El Turk M.Sc., PhD Aalborg University, Denmark Member of I&I Since 2012 2013/2014 SIA Scheme participant Fadi El Turk B.Sc. M.Sc. Research Consultant at Cerner 2013/2014 SIA Scheme participant Previously worked at BMJ Group

Agenda Welcome and introduction Why search and data entry is important (Anne) Examples of Search and Data Entry techniques (Fadi and Anne)

Why is Search and data entry techniques important?

Why are search and data entry techniques important? - SNOMED CT is comprehensive and complex

The expectation: It is a prerequisite that: SNOMED CT can support Semantic interoperability Improved efficiency of clinical care Effective clinical documentation Advanced data analysis Automated decision support Etc. It is a prerequisite that: SNOMED CT coded data is sharable and comparable Search and data entry capabilities are intuitive, effective and efficient

The Problem: Different strategies  different results Different results  coding variability Interrater variability  reduced ability to… Semantic interoperability Improved efficiency of clinical care Effective clinical documentation Advanced data analysis Automated decision support Etc.

Examples of browser differences [Rogers, Jeremy, and Olivier Bodenreider. "SNOMED CT: Browsing the Browsers." KR-MED. 2008.] Examples of browser differences

‘low back pain’

‘pain lower back’

The Problem: Different strategies  different results Different results  coding variability Interrater variability  reduced ability to… Semantic interoperability Improved efficiency of clinical care Effective clinical documentation Advanced data analysis Automated decision support Etc.

CONSISTENT CONCEPT SELECTION A CHALLENGE IN SNOMED CT IMPLEMENTATION Configuration of EHR

CONSISTENT CONCEPT SELECTION A CHALLENGE IN SNOMED CT IMPLEMENTATION Attention must be focused on the consistent application of SNOMED CT. Concept selection needs clear and extensive rules. Necessary to know the content and structure of SNOMED CT. Tooling is important, e.g. more sophisticated browsers. Configuration of EHR CHIANG, Michael F., et al. Reliability of SNOMED-CT coding by three physicians using two terminology browsers. In: AMIA Annual Symposium Proceedings. American Medical Informatics Association, 2006. p. 131. ANDREWS, James E.; RICHESSON, Rachel L.; KRISCHER, Jeffrey. Variation of SNOMED CT coding of clinical research concepts among coding experts. Journal of the American Medical Informatics Association, 2007, 14.4: 497-506. VIKSTRÖM, Anna, et al. Mapping the categories of the Swedish primary health care version of ICD-10 to SNOMED CT concepts: Rule development and intercoder reliability in a mapping trial. BMC Medical Informatics and Decision Making, 2007, 7.1: 1-9.

The Problem: Different strategies  different results Different results  coding variability Interrater variability  reduced ability to…

Reduced ability to... Semantic interoperability Improved efficiency of clinical care Effective clinical documentation Advanced data analysis Automated decision support Etc.

The new IHTSDO ‘Search and Data entry Guide’ Provides recommendations on Search and Data Entry Techniques Developed as part of the ‘SNOMED CT Implementation Advisor Scheme 2013’ Reviewed by the I&I committee Currently under final review by the Head of Implementation and Education To be published by the end of April

Target Audience of the guide Implementers of search and data entry functionality End users of search and data entry ? !

Search vs. data entry

Search use cases Learning about the structure of the terminology Creating reference sets (e.g. to represent subsets of terms and concepts) Creating templates and protocols for data entry Creating queries to retrieve data Reviewing terminology content Data entry

Search guidance Search by words and Identifiers Constrain searches Extend searches Improve search speed Optimize display of search results

Data entry guidance SNOMED CT and structured records Requirements for entry and display of SNOMED CT Constraining data entry Entering refinements for postcoordinated expressions

Examples of search techniques

Search by words and Identifiers

Search by words String searches must be user configurable to support searches, such as searching for: words any order phrase match identical term

Search by identifiers – Searching for cold (1)

Search by identifiers – Searching for cold (2)

Search by identifiers – Searching for cold (3)

Search by identifiers - by Concept ID

Search by identifiers - by Description ID

Constrain searches

Constrain search and data entry by hierarchy Constrain searches by supertype ancestors (e.g. Disorder) 1 2 Recommended as a user configurable option for most use cases

Constrain search and data entry by hierarchy

Constrain search and data entry by reference sets Language ref sets to avoid uncommon or foreign terms Simple ref sets to simplify or encourage selection of concepts or used in a particular country, organization, or specialty Context ref sets to specify or order the valid Concepts for entry in a particular field

Constrain search and data entry by reference sets

Anne  Fadi

Constrain search by status Constrain searches by Concept and Description status Constraining by active Concepts is recommended for data entry use cases There are a few use cases where a user may legitimately wish to search Inactive Concepts and Descriptions (e.g. creating queries for diagnoses for retrospective research).

Constrain searches to avoid multiple hits on the same concept

Extend Searches

Extend searches – Use Word Equivalents (1) In healthcare, there are many words with equivalent meanings Synonyms provide alternative phrases referring to the concept Synonyms are not created automatically for every possible combination of words with an equivalent meaning ‘Renal calculus’ is a synonym of ‘kidney stone’ Search for ‘kidney stone fragmentation’  ‘Percutaneous nephrostomy with fragmentation of kidney stone’ result Search for ‘renal stone fragmentation’  no results.

Extend searches – Use Word Equivalents (2) One way of addressing this problem is to maintain a table of Word Equivalents 1 2

Extend searches: by post-coordinated searching (1) When typing text for a search, the user is unlikely to know if their intended entry can be represented by a single Concept or requires a post-coordinated expression involving additional Concepts or qualifiers. Where searches fail to find a pre-coordinated match, expansion of the search to support appropriate or commonly used qualifiers is likely to enhance usability.

Extend searches: by post-coordinated searching (2) 1

Improving Search speeds

Enable real time searching Improve search speeds Enable real time searching Show an indication of estimated number of matches before starting a search Allow slow searches to be paused or cancelled Optimize indexing Of course you need to index content in order to speed up searches so it is the best way to optimise the way searches are indexed.

OPTIMISING THE DISPLAY OF SEARCH RESULTS

Order Search Results Rationally (1) Order shortest matching results first

Order Search Results Rationally (2) Order preferred term matches before synonyms

Order Search Results Rationally (3) Order user preferred language matches first in multilingual environments

Order Search Results Rationally (5) Display search results with most frequently used descriptions listed first 1 2

Distinguish identical descriptions of different concepts 1 2

Rationalize search results by subsumption checking Before After

Display navigation results effectively (1) Using the subtype hierarchy Not designed for data entry

Display navigation results effectively (2) Using the navigation hierarchy (hand-crafted) Navigation hierarchies can be used to drive some types of structured data entry Navigation hierarchies can order data in sensible ways by priority, or by some readily understood convention (e.g. cranial nerve order).

Discussion

The impact of efficient search capabilities Semantic interoperability Improved efficiency of clinical care Effective clinical documentation Advanced data analysis Automated decision support Etc.

Thank you for your attention Questions? Contact IHTSDO: info@ihtsdo.org Web site: www.ihtsdo.org Anne Randorff Højen: arra@hst.aau.dk Fadi El Turk: Fadi.El-Turk@Cerner.com