1 / 30

Clinical data capture and communication

Clinical data capture and communication. Prof Jeremy Wyatt AMC, Amsterdam j.c.wyatt@amc.uva.nl. Outline. What is clinical data ? What types of data are there ? Why do we collect it ? Data capture or communication ? How reliable is clinical data ? Causes of data errors

varsha
Download Presentation

Clinical data capture and communication

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Clinical data capture and communication Prof Jeremy Wyatt AMC, Amsterdam j.c.wyatt@amc.uva.nl

  2. Outline • What is clinical data ? • What types of data are there ? • Why do we collect it ? • Data capture or communication ? • How reliable is clinical data ? • Causes of data errors • How to reduce data errors • Care pathways as a data collection / communication tool

  3. The cycle of clinical information Clinical practice innovation Knowledge assembly Clinical knowledge Libraries, internet, practice guidelines... informs contributes to informs Quality improvement Reminders, audit & feedback... Evidence base Aetiology, genomics, drug effectiveness... Patient management Diagnosis, investigation, therapy, review... improves informs generates builds Patient data Clinical / lab findings, past history, preferences... Research information management Data management

  4. Abstraction Decision Data Evidence Knowledge Strategy Information What we use to make decisions, inform actions Specific to a case: About the world: Raw information More interpreted

  5. Patient data Patient data: information about a patient and their circumstances relevant to clinical decisions, eg. • Demographics: age, gender, occupation… • Symptoms & signs • Past history, medications • Social history: habits, family, living arrangements • Test results, images • Preferences, beliefs, information-seeking habits… • Hypotheses: problems, differential diagnoses… • Actions: what has been tried, done

  6. Forms that patient data can take • Smells • Sounds • Spoken words • Text • Numbers • Drawings • Images – static, moving • 3D solids - palpation

  7. Sources of patient data • Patient themselves • Carer, relative, friend, other informant • Clinician • Laboratory, imaging dept. • Past records • Other professional: social worker, prison officer, teacher…

  8. Data capture methods Patient – questionnaire, diary, forms, touch screen, email, browser, phone menu system, PDA… Clinician – writing, typing, point and click, dictation / transcription, voice recognition Direct signal, image capture and interpretation Text processing – reports, discharge summaries

  9. Reasons for capturing patient data • For immediate clinical decisions: • By yourself • By others (means communicating the data) • For future clinical decisions: • Means memorising or recording the data • For other purposes: • For quality improvement, education • For clinical research • For management / reimbursement • To act as evidence in a court case

  10. report delivery data capture data processing Data capture or communication ? communication

  11. Clinical coding dept. DoH GP Laboratory, imaging dept. Medical secretary PAS, WL system Main patient data flows in a hospital Record library Paper record Operation note Discharge summary Lab reports Clinical notes Outpatient letters Doctor Pharmacy

  12. Problems of electronic clinical data communication • Urgent lab results missed in 45 A&E cases (XXX) • Names of virology tests muddled in GP systems (HepBSag, anti-HBcAg…) • Still cannot transmit a GP record electronically for 20% of patients who change GPs every year (printed out, re-keyed – A. Majid BMJ 2003)

  13. Aspects of data quality

  14. Some sources of data errors • Mis-identification of patient • Patient recall • Elicitation process – training, definitions • Recording process - transcription • Data transmission • Data processing • Data retrieval • Short term memory (JW will say a number – no cheating !) • Data perception • Data interpretation

  15. Data transcription • Typical figure 4% error rate (Norton ’81), 20% of forms need to be clarified • Best studies: 0.2%, 2% queries • No learning over 20 weeks • Errors increase as no. of choices increase • Role of transcribers: • errors varied from 1-10% between transcribers • rates varied from 30-130 records per hour • transcribers with lowest error rates were fastest

  16. Aspects of errors • Frequency • Size of deviation: • How obvious is it • Examples (Gulliford ’93, cancer registries): • Dates of birth: 93% accurate, mean error 1 day • Radiotherapy dose: 84% accurate, mean error 14 Gray [= 1/3 of normal dose]

  17. What was that number ?

  18. Does electronic communication help ? Systematic review of 176 Medline-listed reports of structured electronic communication to or by GPs 30 articles eligible: 28 described potential effects, only 5 had objective data Results: • Speed of communication: faster in 4 studies • Content of information and records: improved in 2 studies • Quality of care: improved slightly in 1 study (Branger ’99, shared care for diabetes – slight improvement in HbA1C levels) Conclusion:“The literature provides a disappointingly small number of studies that demonstrate improvement of quality of care by means of electronic communication” Van der Kam, Int J Med Inf 2000; 60: 59-70

  19. Can patient data be safely re-used ? Mrs Smith’s glucose was 7.8mM on 1/2/03 Issues: • Patient ID certain ? • Type of specimen: venous / arterial / capillary blood; urine; CSF; pleural fluid… • Actual value (glucometer calibration, local ref. range, artefact…) • Time taken (fasting / not) • Other context – pregnant; disease presence & stage; therapy at the time; previous values… • Is the data relevant anyway ?

  20. Reliability of clinical data • Is the patient pale / jaundiced ? • Is there a heart murmur ? • Is the patient disorientated ? • Does the patient have signs of bronchitis (COAD) ?

  21. Uncertainty in clinical data Signs of a common disease: COAD McAllister F. Why we need large, simple studies of the clinical examination. Lancet 1999; 354

  22. Solution • Review existing studies, carry out an SR, eg. JAMA Rational Clinical Exam series – CAGE score to detect alcoholism, diagnosis of DVT • Where past studies permit no sound conclusions, develop a protocol and carry out a new large study using the internet – Clinical Assessment of the Reliability of the Examination • See CARE: www.carestudy.com/

  23. Reasons for inaccurate data • Error: “Patients with terminal cancer” who had neither serious illness nor cancer – Rees, BMJ 1987 • System error: • Haste (NAD – not actually done ?) • Missing cases notes – halved mortality in low birthweight babies cf. prospective study (Westgren ’86) • Reimbursement – GPs record procedures on which incentive payments depend (Pringle ’91) • Other: • Pre-surgery renal failure rate in cardiac patients jumped 6 X when NY State published surgeon-specific death rates (Green ’95) • Patient diaries: ¼ of entries more than 6 hours after intended time or invented (Chowienczyck ’94)

  24. Some sources of error Search mis-analysis Lab report poor criteria mis-filing transcription, coding errors Paper record Computer record Patient poor search mis-informing mis-recording Clinical finding Result hard to interpret Inference

  25. Studies on records Compare written record to tape of consultation: • Main complaint: 92% complete • History of present illness: 72% • Other history: 29% • Advice on smoking: 29%

  26. Improving data capture • Problem-specific checklists • Standardised, validated wording and question order • Clear prompts & definitions, space for comments • Direct coding of answers – check boxes, tabular foms (Fries ’74) • Absolute and “verify” range checks; consistency checks across fields; check on legal values • Redundancy: check digits, double keying • Compensation: uppercase only + Soundex algorithm to match names • Make the form the only record & provide incentives to use it – care pathways

  27. Care pathways Problem-specific multi-professional record (for eg. hip replacement, heart attack, stroke) with: • Space to record data, actions, initials • Space to record reasons why data not collected / actions not carried out (“variance tracking”) • Key guideline recommendations, reminders, scoring systems Describe the default course of a patient through the health system, day by day Can share with patients / carers See: www.nelh.nhs.uk/carepathways/

  28. The aim of ICPs To have... • the right people • doing the right things • in the right order • at the right time • in the right place • with the right outcome • all with attention to the patient experience • and to compare planned care with care actually given (NeLH ICP web site)

  29. Further reading Wyatt JC. Acquisition & use of clinical data for audit and research. J Evaluation in Clin Practice 1995; 1: 15-27 Wyatt JC. Clinical data systems series, Lancet 1994: Part I: Data and medical records. Lancet 1994; 344: 1543-47 Part II: Components and techniques. Lancet 1994; 344: 1609-1614 Part III: Developing and evaluating clinical data systems. Lancet 1994; 344: 1682-88

More Related