PMID- 26245239
OWN - NLM
STAT- MEDLINE
DCOM- 20160517
LR  - 20191210
IS  - 2058-4563 (Electronic)
IS  - 2058-4555 (Linking)
VI  - 22
IP  - 2
DP  - 2015 Jun 5
TI  - An algorithm to improve diagnostic accuracy in diabetes in computerised problem 
      orientated medical records (POMR) compared with an established algorithm 
      developed in episode orientated records (EOMR).
PG  - 255-64
LID - 10.14236/jhi.v22i2.79 [doi]
AB  - BACKGROUND: An algorithm that detects errors in diagnosis, classification or 
      coding of diabetes in primary care computerised medial record (CMR) systems is 
      currently available. However, this was developed on CMR systems that are episode 
      orientated medical records (EOMR); and do not force the user to always code a 
      problem or link data to an existing one. More strictly problem orientated medical 
      record (POMR) systems mandate recording a problem and linking consultation data 
      to them. OBJECTIVE: To compare the rates of detection of diagnostic accuracy 
      using an algorithm developed in EOMR with a new POMR specific algorithm. METHOD: 
      We used data from The Health Improvement Network (THIN) database (N = 2,466,364) 
      to identify a population of 100,513 (4.08%) patients considered likely to have 
      diabetes. We recalibrated algorithms designed to classify cases of diabetes to 
      take account of that POMR enforced coding consistency in the computerised medical 
      record systems [In Practice Systems (InPS) Vision] that contribute data to THIN. 
      We explored the different proportions of people classified as having type 1 
      diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) and with diabetes 
      unclassifiable as either T1DM or T2DM. We compared proportions using chi-square 
      tests and used Tukey's test to compare the characteristics of the people in each 
      group. RESULTS: The prevalence of T1DM using the original EOMR algorithm was 
      0.38% (9,264/2,466,364), and for T2DM 3.22% (79,417/2,466,364). The prevalence 
      using the new POMR algorithm was 0.31% (7,750/2,466,364) T1DM and 3.65% 
      (89,990/2,466,364) T2DM. The EOMR algorithms also left more people unclassified 
      11,439 (12%), as to their type of diabetes compared with 2,380 (2.4%), for the 
      new algorithm. Those people who were only classified by the EOMR system differed 
      in terms of older age, and apparently better glycaemic control, despite not being 
      prescribed medication for their diabetes (p < 0.005). CONCLUSION: Increasing the 
      degree of problem orientation of the medical record system can improve the 
      accuracy of recording of diagnoses and, therefore, the accuracy of using 
      routinely collected data from CMRs to determine the prevalence of diabetes 
      mellitus; data processing strategies should reflect the degree of problem 
      orientation.
FAU - de Lusignan, Simon
AU  - de Lusignan S
AD  - University of Surrey, Guildford, UK. s.lusignan@surrey.ac.uk.
FAU - Liaw, Siaw-Teng
AU  - Liaw ST
AD  - University of New South Wales, Australia.
FAU - Dedman, Daniel
AU  - Dedman D
AD  - Clinical Practice Research Datalink, London, UK.
FAU - Khunti, Kamlesh
AU  - Khunti K
AD  - University of Leicester, Leicester, UK.
FAU - Sadek, Khaled
AU  - Sadek K
AD  - University of Surrey, Guildford, UK.
FAU - Jones, Simon
AU  - Jones S
AD  - University of Surrey, Guildford, UK.
LA  - eng
PT  - Comparative Study
PT  - Evaluation Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20150605
PL  - England
TA  - J Innov Health Inform
JT  - Journal of innovation in health informatics
JID - 101651620
SB  - IM
MH  - *Algorithms
MH  - Diagnosis, Differential
MH  - *Electronic Health Records
MH  - *Episode of Care
MH  - Humans
MH  - *Medical Records, Problem-Oriented
OTO - NOTNLM
OT  - computerized
OT  - diabetes mellitus
OT  - epidemiology
OT  - medical record systems
OT  - medical records
OT  - problem-oriented
OT  - records as topic
EDAT- 2015/08/08 06:00
MHDA- 2016/05/18 06:00
CRDT- 2015/08/07 06:00
PHST- 2014/06/05 00:00 [received]
PHST- 2014/10/26 00:00 [accepted]
PHST- 2015/08/07 06:00 [entrez]
PHST- 2015/08/08 06:00 [pubmed]
PHST- 2016/05/18 06:00 [medline]
AID - 10.14236/jhi.v22i2.79 [doi]
PST - epublish
SO  - J Innov Health Inform. 2015 Jun 5;22(2):255-64. doi: 10.14236/jhi.v22i2.79.