Rescuing Data from Decaying and Moribund Clinical Information Systems

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Professor Jon Patrick Health Information Technology Research Laboratory (HITRL - www.it.usyd.edu.au/~hitru) School of Information Technologies University of Sydney (P39, 17/10/08, Systems & Methods stream, 1.50pm)

Health & Medicine Technology Business

Rescuing Data from Decaying and Moribund Clinical Information Systems Jon Patrick, Peng Gao, Xin Li, Victor Zhou Health Information Technology Research Laboratory School of Information Technologies www.it.usyd.edu.au/~hitru

Objectives ,[object Object],[object Object]

Methods ,[object Object],[object Object],[object Object]

Data Model Reverse Engineering (DMRE ) ,[object Object],[object Object],[object Object],[object Object],[object Object]

Data Model Minimisation (DMM) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Data Migration (DM) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Discussion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Clinical Data Analytics Language - CliniDAL ,[object Object],[object Object],[object Object],[object Object]

CliniDAL Glossary ,[object Object],[object Object],[object Object],[object Object]

Using TTSCT to identify Snomed concepts within clinical texts

Project objectives ,[object Object],[object Object]

CliniDAL indexing process ,[object Object],[object Object],[object Object],Request_id is being used as an internal unique id in the SWAPS database referring to every single patient medical record. Request_id_list records the list of request ids whose text contains a specific concept and freq records how many times the concept appeared on the corresponding text. Description Text Text Integer Text Text Datetime Datetime Datetime Conceptid Request_id_list Frequency Version_id Index_created_date_time First_seen_date_time Last_seen_date_time Data_type Column_name

Some statistics from the index ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Most frequent concept top 10 37.84% 151357 One (qualifier value) 38112003 39.12% 156476 On examination - soft tissue swelling-local (finding) 164600000 39.12% 156482 mL/s/100mL of tissue (qualifier value) 259075005 39.12% 156482 Tissue used (attribute) 272742005 39.12% 156487 Structure of peri-ileal tissue (body structure) 81743008 39.12% 156487 Structure of periesophageal tissue (body structure) 113282007 39.12% 156488 Body tissue structure (body structure) 85756007 39.12% 156489 Entire periesophageal tissue (body structure) 322937007 39.12% 156489 Entire peri-ileal tissue (body structure) 264454007 40.50% 162009 Histocompatibility crossmatch (procedure) 72845001 Frequency Occurrences Concept Description Concept ID

CliniDAL on SWAPS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Acknowledgements ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Bioinformaticians constantly face challenges with data: from the large volumes of data to the need to integrate diverse data types. Relational databases have a long and successful history of managing data but have been unable to meet emerging needs of big data and highly integrated data stores. This talk discusses the limitations we face when using relational data models for bioinformatics applications. It describes the features, limitations and use cases of four alternative database models: key value databases, document databases, wide column data stores and graph databases. Use in bioinformatics applications is demonstrate with text mining and atherosclerosis research projects. The talk concludes with guidance on choosing an appropriate database model for varying bioinformatics requirements.

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This one also I presented at the HINZ conference. ABSTRACT: Use of health information for multiple purposes maximises its value. A good example is PREDICT, a clinical decision support system which has been used in New Zealand for a decade. Collected data are linked and enriched with a number of databases, including national collections, laboratory tests and pharmacy dispensing. We are proposing a new model-driven approach for data management based on openEHR Archetypes for the purpose of improving data linkage and future-proofing of data. The study looks at feasibility of building a content model for PREDICT - a methodology underpinning the Interoperability Reference Architecture. The main premise of the content model will be to provide a canonical model of health information which will be used to align incoming data from other data sources. With this approach it is possible to extend datasets without breaking semantics over long periods of time – a valuable capability for research. The content model was developed using existing archetypes from openEHR and NEHTA repositories. Except for two checklist type items, reused archetypes can faithfully represent the whole PREDICT dataset. The study also revealed we will need New Zealand specific extensions for demographic data. Use of archetype based content modelling can improve secondary use of clinical data.

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sis of health condition is very challenging task for every human being because life is directly related to health condition. Data mining based classification is one of the important applications for classification of data. In this research work, we have used various classification techniques for classification of thyroid data. CART gives highest accuracy 99.47% as best model. Feature selection plays very important role to computationally efficient and increase the performance of model. This research work focus on Info Gain and Gain Ratio feature selection technique to reduce the irrelevant features from original data set and computationally increase the performance of model. We have applied both the feature selection techniques on best model i. e. CART. Our proposed CART-Info Gain and CARTGain Ratio gives 99.47% and 99.20% accuracy with 25 and 3 feature respectively.

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Rescuing Data from Decaying and Moribund Clinical Information Systems

1. Rescuing Data from Decaying and Moribund Clinical Information Systems Jon Patrick, Peng Gao, Xin Li, Victor Zhou Health Information Technology Research Laboratory School of Information Technologies www.it.usyd.edu.au/~hitru

3. Background

8. Figure 2

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11.

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14. Using TTSCT to identify Snomed concepts within clinical texts

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18. Most frequent concept top 10 37.84% 151357 One (qualifier value) 38112003 39.12% 156476 On examination - soft tissue swelling-local (finding) 164600000 39.12% 156482 mL/s/100mL of tissue (qualifier value) 259075005 39.12% 156482 Tissue used (attribute) 272742005 39.12% 156487 Structure of peri-ileal tissue (body structure) 81743008 39.12% 156487 Structure of periesophageal tissue (body structure) 113282007 39.12% 156488 Body tissue structure (body structure) 85756007 39.12% 156489 Entire periesophageal tissue (body structure) 322937007 39.12% 156489 Entire peri-ileal tissue (body structure) 264454007 40.50% 162009 Histocompatibility crossmatch (procedure) 72845001 Frequency Occurrences Concept Description Concept ID

19. CliniDAL system architecture

20. CliniDAL interface

21. CliniDAL query result sample

22.

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Rescuing Data from Decaying and Moribund Clinical Information Systems

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