eHealth Unit
Institute of Digital Health Launch
22 June 2015
Why eHealth?
• People are living
longer, &
• People survive
illnesses that used to
kill them
– Better living conditions
– New health
technologies (Drugs,
Investigations, Rx)
Why eHealth?
• So health care costs
go up,
• But budgets don’t (as
much) (£113bn 201415)
• Need to “do more, with
less”
• Staff (1.4m) = largest
single NHS expense
eHealth Unit
• Established 2003
• “To explore the use of new information and
communication technologies, such as the internet
and mobile phones, to improve health and health
care”
• Multi-disciplinary, strong PPI, grown rapidly
• 3 work streams
Developing & evaluating
interventions for patients / the
public:
• Health promotion (sexual health for young people)
• Behaviour change (alcohol reduction)
• Self-management of long term conditions (type 2
diabetes)
2. Implementation of eHealth into routine care
• Theory development
Normalization Process
Theory
• Toolkits to promote
implementation
(e-HIT; NPT toolkit)
• Empirical work
(Systematic reviews;
Alcohol; Diabetes;).
electronic care
records:
“significantly behind
schedule and costs
have escalated”;
“unworkable”.
“Costs significantly
outweigh benefits”;
“Up to 98% of
benefits yet to be
realised”
NAO 2013
PAC 2011
3. Impact of eHealth on Health Care
Professional – patient interactions.
•
•
•
•
“Therapeutic relationship” at the heart of medicine
HCP & patients work hard at it
Interactions are altered by technology
Interviews & video recordings of interactions:
–
–
–
–
What happens?
Why it happens?
What do patients and HCP want to happen?
How can we facilitate this?
Study or Subgroup
1.1.1 students
Impact: Alcohol
• DownYourDrink (DYD):
– Freely available online since
2003;
– C. 50,000 registered users
• DYD Kingston
– Tier 2 & 3 treatment for
alcohol in Kingston
Bewick 2008
Chiauzzi 2005
Doumas 2008b
Kypri 2004
Kypri 2008
Lau-Barraco 2008
Lewis 2007a
Lewis 2007b
Neighbors 2004
Neighbors 2006
Walters 2007
Weitzel 2007
Subtotal (95% CI)
Weight
Mean Difference
IV, Random, 95% CI
8.3%
6.0%
7.0%
7.3%
8.0%
6.2%
9.5%
10.3%
8.7%
7.4%
8.4%
1.7%
88.9%
-22.64 [-51.05, 5.77]
-15.17 [-58.09, 27.75]
-7.47 [-43.90, 28.96]
15.00 [-19.69, 49.69]
-32.93 [-63.08, -2.78]
-14.47 [-56.21, 27.27]
-28.51 [-50.39, -6.63]
-43.44 [-61.00, -25.88]
-17.28 [-43.50, 8.94]
-10.04 [-43.69, 23.61]
2.22 [-26.07, 30.51]
-13.77 [-118.73, 91.19]
-19.42 [-29.83, -9.00]
Mean Difference
IV, Random, 95% CI
Heterogeneity: Tau² = 89.12; Chi² = 15.23, df = 11 (P = 0.17); I² = 28%
Test for overall effect: Z = 3.65 (P = 0.0003)
1.1.2 non-students
Hester 1997
Hester 2005
Neumann 2006
Riper 2008
Subtotal (95% CI)
1.1% -242.56 [-376.51, -108.61]
2.8% -132.30 [-210.14, -54.46]
4.2%
-16.03 [-74.47, 42.41]
2.9% -119.00 [-194.39, -43.61]
11.1% -114.94 [-198.60, -31.29]
Heterogeneity: Tau² = 5374.82; Chi² = 12.83, df = 3 (P = 0.005); I² = 77%
Test for overall effect: Z = 2.69 (P = 0.007)
Total (95% CI)
100.0%
-25.88 [-40.78, -10.98]
Heterogeneity: Tau² = 481.91; Chi² = 39.26, df = 15 (P = 0.0006); I² = 62%
-500
-250
0
250
500
Test for overall effect: Z = 3.40 (P = 0.0007)
Favours experimental Favours control
Khadjesari et al. Addiction 2010;106:267-82
• HeLP-Alcohol
– CLAHRC funding to compare
effectiveness with face to face
treatment & how best to
integrate into routine care
Impact: Diabetes
• NIHR PGfAHR
• Implementation study
• Part of Diabetes
pathway in 2 CCG
• Community Interest
Company
• 6 Contracts this year
• Awards include: HSIP;
mHealth Unawards;
UCL Enterprise
Conclusions
• eHealth has great potential
• As yet, largely unrealised
• Major problems with:
– Effectiveness
– Engagement
– Integration into care
• Our research tackles these problems, adopting
multi-disciplinary and mixed methods approach