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The evidence for digital
            healthcare

      Jeremy Wyatt and Kevin Yap
Questions about digital health technologies

     Question             Information needed               Type of study
1. Can it be done ? That a prototype exists        Feasibility study
2. Is it safe ?       Hazard analysis; accuracy of Inspection eg. HAZOP; pilot
                      data, advice etc.            tests of accuracy, usability
3. Is it acceptable? User attitudes, usage rates   Interviews, survey, log file
                                                   analysis
4. Is it clinically   Size of benefit vs. harms    Pragmatic randomised trial
   effective ?
5. Is it cost         Incremental cost             NICE technology appraisal
   effective?         effectiveness ratio
“We know it works”




“… Full advanced life-support did not decrease mortality or
morbidity... during advanced life-support, mortality was greater
among patients with Glasgow Coma Scale scores < 9”
Stiell IG et al. CMAJ. 2008; 178: 1141-52

Solution: do a trial - Liu & Wyatt, JAMIA 2011
Plausible DHC technologies that failed

 Diagnostic decision support (Wyatt, MedInfo ‘89)
 Integrated medicines management for a children’s
  hospital (Koppel, JAMA 2005)
 MSN messenger triage (Eminovic, JTT 2006)
 Telehealth for COPD (Polisena, JMIR 2010)
 Smart home applications ? (Martin, CDSR 2008):
  “The effects of smart technologies to support
  people in their homes are not known. Better quality
  research is needed.”
Type of studies found by Eminovic
MSN messenger chat with NHS Direct nurse
The serious games evidence base
   1 randomised trial:
    – 91 learners about triage randomly allocated to serious game
      or card sort control
    – Outcomes: skill at triage of 8 simulated cases
    – Results:
        • Accuracy (0 or 1 errors on 8 cases): 91% game, 80% control (p=0.02)
        • Time taken: game 456, card sort control 435, p=0.155

    (Knight, de Freitas, Dunwell et al. Resuscitation 2010; 81: 1175-9)



   0 Systematic reviews
Cochrane review on phone consultation / triage

 Range of study types: 5 RCTs, 1 CCT, 3 ITS up to 2007
 Results: 3/5 studies on GP visits showed drop, but 2
  showed rise in later visits
 Of 7 studies on A&E usage, 6 showed no change & 1
  showed an increase
 2 studies looked at deaths – no difference
 “Phone consultation appears to reduce the no. of
  surgery contacts & OOH visits by GPs, but questions
  remain on service use, safety, cost & pt. satisfaction”

(Burin et al, CDSR 2010)
e-prescribing & adverse drug events




  4 studies showed significant reduction in ADEs by 30-84%
  1 study showed non-sig. increase in risk by 9%
[1] Ammenwerth E, et al. JAMIA 2008;15(5):585-600.
TeleHealth in diabetes, bronchitis & heart failure
Diabetes (Farmer et al SR, 2005):
   – Slight reduction of HbA1C by 0.1% (95% CI -0.4% to 0.04%)
   – Use of services no different or increased with telehealth

Bronchitis (Polisena et al SR, 2010):
   – Mortality may be greater in telephone-support group (RR = 1.2;
     95% CI 0.84 to 1.75)
   – Reduced hospitalization and A&E visits, but impact on hospital
     bed days varied

Heart failure (Inglis et al, CDSR 2010):
   – Reduced mortality by 44% (RR 0.66, CI 0.54-0.81, p < 0.001)
   – Reduced CHF-related admissions by 23% (RR 0.77)
   – However, recent large RCT negative (Chaudry NEJMed, Dec 2010)
The evidence base for digital healthcare ?
                        Quality   Safety   Prevention   Productivity
  Electronic records       +        ++        ++            +/-
  Phone consultations     ++       +/-         -            ++
  Email, SMS               +       +/-         -            +/-
  Decision support        +++       ++        ++            ++
  Telemedicine             +        +          -             +
  Remote monitoring       +/-       -          -             +
  Serious games            +        +          +            +/-
  Virtual reality          +        +         +/-            +


Importance of context:
• Setting: community, primary, secondary care
• Users: nurses, doctors, therapists, the patient
• Care groups – age, disease, severity…
• Other benefits: improved patient access, education
Case study: decision support systems


“A knowledge-rich system that processes two or more
items of patient data to generate encounter-specific
advice or interpretation” Wyatt & Spiegelhalter, 1991

Used to generate:
 Advice about diagnosis
 Prescribing alerts & reminders
 Interpretation of lab test results…
New errors introduced by DSS
   Inappropriate drug form selected for route
    – e.g. capsules for IV administration
   Inappropriate product selected
   Incorrect dose, frequency, formulation from
    dropdown menu
   Inappropriate selection of default doses
   Missed drug allergies / high severity interactions
    – high override rate
   Duplicate orders
    – system could not distinguish regular, one off & PRN orders
   Failure to stop drugs that are no longer required
   Increased drug monitoring errors
    – eg. fail to include diluents if CV line not selected
14/30
Reasons for negative ACORN trial

 ACORN was too slow (1986 !) so advice
  given too late
 Nurses not empowered to act on advice
 Cardiac care unit always full
 ACORN also used in 15% of controls
 Patients, not nurses, randomised –
  learning effect
When do decision support systems work ?

                                    Success rates across trials
Target clinical practice       Clinical practice     Patient outcomes
Diagnosis                     40%            4/10     0%          0/5
Disease management            62%           23/37    18%          5/27

Single drug prescribing,      62%           15/24    11%          2/18
dosing
Prevention                    76%           16/21     0%          0/1
Multi-drug prescribing        80%             4/5     0%          0/4

Overall                       64%           62/97    13%          7/52



               Garg et al, JAMA 2005, 293: 1223-38
Home-grown vs commercial systems


                              Commercial: 96%
                              reduction to 26%
                                increase (NS)




                             Home grown: strong
                              effect 99% to 16%
                              reduction (p<0.05)



Ammenwerth E, et al. JAMIA 2008;15(5):585-600.
Cost effectiveness of DSS to advise
on warfarin dosage
 Clinically effective & promote safety: trials show 3-
  13% increase in patient time within therapeutic
  range
 Very cost effective: incremental cost effectiveness
  ratio £2200 per quality adjusted life year (NICE
  technology appraisal 2005)
Conclusions
1. Technology can harm as well as help
2. Evidence is often context sensitive
3. Some evidence of effectiveness, little on cost
   effectiveness / savings
4. The wrong kind of evaluation studies (eg. Chaudry)
   can mislead
5. Working with EU Commission to improve design of
   evaluation studies in Framework 7/8 projects
6. IDH will be publishing evidence reviews on selected
   technologies over next few months

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Evidence in Digital Healthcare

  • 1. The evidence for digital healthcare Jeremy Wyatt and Kevin Yap
  • 2. Questions about digital health technologies Question Information needed Type of study 1. Can it be done ? That a prototype exists Feasibility study 2. Is it safe ? Hazard analysis; accuracy of Inspection eg. HAZOP; pilot data, advice etc. tests of accuracy, usability 3. Is it acceptable? User attitudes, usage rates Interviews, survey, log file analysis 4. Is it clinically Size of benefit vs. harms Pragmatic randomised trial effective ? 5. Is it cost Incremental cost NICE technology appraisal effective? effectiveness ratio
  • 3. “We know it works” “… Full advanced life-support did not decrease mortality or morbidity... during advanced life-support, mortality was greater among patients with Glasgow Coma Scale scores < 9” Stiell IG et al. CMAJ. 2008; 178: 1141-52 Solution: do a trial - Liu & Wyatt, JAMIA 2011
  • 4. Plausible DHC technologies that failed  Diagnostic decision support (Wyatt, MedInfo ‘89)  Integrated medicines management for a children’s hospital (Koppel, JAMA 2005)  MSN messenger triage (Eminovic, JTT 2006)  Telehealth for COPD (Polisena, JMIR 2010)  Smart home applications ? (Martin, CDSR 2008): “The effects of smart technologies to support people in their homes are not known. Better quality research is needed.”
  • 5. Type of studies found by Eminovic
  • 6. MSN messenger chat with NHS Direct nurse
  • 7. The serious games evidence base  1 randomised trial: – 91 learners about triage randomly allocated to serious game or card sort control – Outcomes: skill at triage of 8 simulated cases – Results: • Accuracy (0 or 1 errors on 8 cases): 91% game, 80% control (p=0.02) • Time taken: game 456, card sort control 435, p=0.155 (Knight, de Freitas, Dunwell et al. Resuscitation 2010; 81: 1175-9)  0 Systematic reviews
  • 8. Cochrane review on phone consultation / triage  Range of study types: 5 RCTs, 1 CCT, 3 ITS up to 2007  Results: 3/5 studies on GP visits showed drop, but 2 showed rise in later visits  Of 7 studies on A&E usage, 6 showed no change & 1 showed an increase  2 studies looked at deaths – no difference  “Phone consultation appears to reduce the no. of surgery contacts & OOH visits by GPs, but questions remain on service use, safety, cost & pt. satisfaction” (Burin et al, CDSR 2010)
  • 9. e-prescribing & adverse drug events 4 studies showed significant reduction in ADEs by 30-84% 1 study showed non-sig. increase in risk by 9% [1] Ammenwerth E, et al. JAMIA 2008;15(5):585-600.
  • 10. TeleHealth in diabetes, bronchitis & heart failure Diabetes (Farmer et al SR, 2005): – Slight reduction of HbA1C by 0.1% (95% CI -0.4% to 0.04%) – Use of services no different or increased with telehealth Bronchitis (Polisena et al SR, 2010): – Mortality may be greater in telephone-support group (RR = 1.2; 95% CI 0.84 to 1.75) – Reduced hospitalization and A&E visits, but impact on hospital bed days varied Heart failure (Inglis et al, CDSR 2010): – Reduced mortality by 44% (RR 0.66, CI 0.54-0.81, p < 0.001) – Reduced CHF-related admissions by 23% (RR 0.77) – However, recent large RCT negative (Chaudry NEJMed, Dec 2010)
  • 11. The evidence base for digital healthcare ? Quality Safety Prevention Productivity Electronic records + ++ ++ +/- Phone consultations ++ +/- - ++ Email, SMS + +/- - +/- Decision support +++ ++ ++ ++ Telemedicine + + - + Remote monitoring +/- - - + Serious games + + + +/- Virtual reality + + +/- + Importance of context: • Setting: community, primary, secondary care • Users: nurses, doctors, therapists, the patient • Care groups – age, disease, severity… • Other benefits: improved patient access, education
  • 12. Case study: decision support systems “A knowledge-rich system that processes two or more items of patient data to generate encounter-specific advice or interpretation” Wyatt & Spiegelhalter, 1991 Used to generate:  Advice about diagnosis  Prescribing alerts & reminders  Interpretation of lab test results…
  • 13. New errors introduced by DSS  Inappropriate drug form selected for route – e.g. capsules for IV administration  Inappropriate product selected  Incorrect dose, frequency, formulation from dropdown menu  Inappropriate selection of default doses  Missed drug allergies / high severity interactions – high override rate  Duplicate orders – system could not distinguish regular, one off & PRN orders  Failure to stop drugs that are no longer required  Increased drug monitoring errors – eg. fail to include diluents if CV line not selected
  • 14. 14/30
  • 15. Reasons for negative ACORN trial  ACORN was too slow (1986 !) so advice given too late  Nurses not empowered to act on advice  Cardiac care unit always full  ACORN also used in 15% of controls  Patients, not nurses, randomised – learning effect
  • 16. When do decision support systems work ? Success rates across trials Target clinical practice Clinical practice Patient outcomes Diagnosis 40% 4/10 0% 0/5 Disease management 62% 23/37 18% 5/27 Single drug prescribing, 62% 15/24 11% 2/18 dosing Prevention 76% 16/21 0% 0/1 Multi-drug prescribing 80% 4/5 0% 0/4 Overall 64% 62/97 13% 7/52 Garg et al, JAMA 2005, 293: 1223-38
  • 17. Home-grown vs commercial systems Commercial: 96% reduction to 26% increase (NS) Home grown: strong effect 99% to 16% reduction (p<0.05) Ammenwerth E, et al. JAMIA 2008;15(5):585-600.
  • 18. Cost effectiveness of DSS to advise on warfarin dosage  Clinically effective & promote safety: trials show 3- 13% increase in patient time within therapeutic range  Very cost effective: incremental cost effectiveness ratio £2200 per quality adjusted life year (NICE technology appraisal 2005)
  • 19. Conclusions 1. Technology can harm as well as help 2. Evidence is often context sensitive 3. Some evidence of effectiveness, little on cost effectiveness / savings 4. The wrong kind of evaluation studies (eg. Chaudry) can mislead 5. Working with EU Commission to improve design of evaluation studies in Framework 7/8 projects 6. IDH will be publishing evidence reviews on selected technologies over next few months

Notas del editor

  1. Effects of e-Rx on potential ADEs:*Sig. RR from 35% to 98% (6/9, RR=0.02-0.65)*Sig. RR of 29% (1/9, RR=1.29, 95%CI: 1.04-1.60): [Mitchell D, et al. J Inform Tech Healthcare 2004;2(1): 19-29]  small study*Inconclusive result (2/9): RR 0.63, 95%CI: 0.38-1.05 [Gandhi 2005] &amp; RR 0.97, 95%CI: 0.16-5.82 [Bizovi 2002] small studies [Gandhi TK, et al. J Gen Intern Med 2005;20(9):837-841, Bizovi KE, et al. AcadEmerg Med 2002;9(11):1168-1175]Effects of e-Rx on ADEs:*Sig. RR from 30% to 84% (4/6, RR=0.16-0.70)*Non-sigRR of 13% (1/6, RR=0.87, 95%CI: 0.39-1.94) [Mullett CJ, et al. Pediatrics 2001;108(4):E75]*Small &amp; non-sig. RR of 9% (1/6, RR=1.09, 95%CI: 0.92-1.29): [Bates DW, et al. JAMA 1998:280(15):1311-1316]
  2. Home-grown systemsvs Commercial systems:* Higher relative risk reduction (RRR) by home-grown than commercial systems.*Home-grown CPOEs (11/23): Sig. RR from 13% to 99% (RR=0.01-0.87, 95%CI: 0.00-0.94)*Commercial CPOEs (12/23): Non-sig changes, ranges from 96% RR to 26% RR (RR=0.04-1.26, 95%CI: 0.00-1.55)** Reason: Home-grown systems can be modified &amp; adapted more easily and quickly to local needs.