Meeting with Eat4Health Co-ordinator

Agreed price of Eat4Health (E4H) sessions

Each Scheme = 10 “sessions” of 1.5hrs with 12-15 participants each.

Price 30 pounds (*10 = 300) plus 100 pounds total travel time.  Plus Accommodation costs either at Surgeries or Local Leisure Centre. (So 400 per scheme plus accommodation)

Anticipate 15 Schemes in all, which will sometimes run concurrently so patients from each of the surgeries can cross over.

First Schemes to start mid April 2013

Once the “Lifestyle” Questionnaire is in place the patient letter will be complete, the searches are in place and we will be ready to go live with Each of the EmisWEB practices as they go live.

HSJ Article mentions the PreDM project

The original article in the HSJ is password protected but here is a copy

Information overload: CCGs and data innovation

14 February, 2013 | By Sean Riddell

Clinical commissioning groups have more data available to them than ever before. Sean Riddell looks at how it can be used to deliver real change.

We are living in the “open data” era. The government has committed to making more and more data freely available: with over 40,000 files, data.gov.uk is said to be the largest data resource in the world.

Within the NHS, the transparency agenda has seen more data than ever before made public − including quarterly prescribing reports for every primary care trust in England and detailed analyses of hospital spending on drugs for conditions such as HIV and AIDS.

Clinical commissioning groups and individual GPs are also feeling the impact of the drive for data. A significant milestone is the launch of the GP Extraction Service  − which will, for the first time, release national data from GP records to try to improve patient care and efficiencies.

‘For clinicians, the only way to effect change is to integrate the derived knowledge into their clinical management system’

CCGs are also beginning to contract bespoke data extraction services to help them gain a better and deeper understanding of local health services. This data can be used for a multitude of different purposes − from creating risk profiling dashboards that identify patients at risk of unscheduled hospital admission, to monitoring prescribing spend across a locality.

But how does the drive for “open data” translate into real change? Liberating the data is only the first step. The information then has to be translated into knowledge and, finally, integrated into clinical workflows if it is to make a difference on the front line.

Data disconnect

Data is not much use in isolation – it’s what you do with it that matters. There is a real risk of a data disconnect if clinicians are bombarded with more and more data without any intelligent interpretation or attempt to integrate it with how they work.

Let’s consider the typical GP, who will regularly receive many different pieces of information and guidance about how they should be working − including, for example, the latest National Institute for Health and Clinical Excellence guidelines or new local protocols for specialist referrals.

While this information is useful and relevant for GPs, the way it is communicated − typically on paper − is a disconnect with their largely electronic workflow.

Most GPs spend their entire working day within a single clinical software system. For these clinicians, the only way to effect change is to integrate your derived knowledge into their clinical management system. By making it a seamless part of how they do their job, you can start to make a difference.

Making the data work

A good example of how this can work is an innovative project carried out within a leading GP clinical system to identify patients with undiagnosed diabetes.

Following work with a national research body, the software provider embedded a new algorithm into its software. The algorithm automatically interrogates patients’ medical records, searching for blood test results that show a high blood sugar (HbA1C) level but no clinical diagnosis of diabetes.

‘Identifying at-risk patients is still only part of the story − the real test is what you do about it’

During consultations, the software highlights these patients via an automated alert reminding the GP to investigate the reason for their high blood sugar. The alert remains on the system until the GP confirms an investigation and conclusion (ie the patient has diabetes or there is another reason for the HbA1C score).

Because it is fully integrated into the GP’s clinical system, this alert becomes a seamless part of the way the GP does their job − in this way it starts to effect change.

At a national level, the software provider estimates that the algorithm could identify 57,000 patients with diabetes, based on a high blood sugar level which has not been followed up.

Of course, identifying at risk patients is still only part of the story − the real test is what you do about it. This brings me to my final point about how data can improve healthcare.

Evidence-based IT

Integrating new ways of working into clinical software is a huge step forward but it’s not the end of the data trail. In my opinion, the real power of data can only be measured when you close the loop and measure what difference your intervention has made. I call this evidence-based IT.

Here is another innovative example of how data is being harnessed to deal with the nation’s diabetes epidemic. At the Falkland Surgery in Berkshire, which has 14,500 patients, GPs are using QDiabetes, a stratified risk predictor embedded within their clinical IT system, to identify patients with a one-in-five risk of developing diabetes in the next 10 years.

‘With all patients there was a definite psychological sense of hope and enthusiasm that was not there at the beginning’

Having identified the most at risk patients, the GPs compared the effectiveness of different interventions. Fifty-two patients were invited to take part in either a healthy eating or a healthy exercise regime to help them lose weight and increase activity to head off the disease.

The initial results showed good compliance in both groups, and measurable improvements for a majority of patients after 10 weeks, including weight loss and reduced waist circumference.

Although not statistically significant, the results were encouraging enough for the initiative to be proposed for roll out across 12 other practices with a total 100,00 patients in the locality. The Newbury and District CCG is considering making it part of its quality, innovation, productivity and prevention programme.

Project lead Dr Tim Walter said: “The results are very promising, and we are delighted that this important preventative work in a key group of patients is likely to be tested elsewhere.

“What was very interesting was the psychological impact, which we didn’t set out to measure. At a follow-up meeting with all the patients there was a definite psychological sense of hope and enthusiasm that was not there at the beginning.”

Find out more

Sean Riddell is chief executive at the EMIS Group

All surgeries signed up

Today we confirmed all the local surgeries have agreed to participate in the project.  This includes two INPS, and two SystmOne surgeries (live or converting) and seven Emis Practices who are all transferring to EMisWEB in the next few months.

Next step to sort out individual timelines for each practice to ensure the E4H lifestyle trainers are used effectively.

Library Search for Attitudinal Questionnaires

One aspect of the process I struggled with was how we could assess the characteristics of the attendees of the intervention course.  I asked our local Medical Library at Prospect Park, Reading and got a fantastic list of potential references listed below.  As we refine the process I’ll create a shortlist and put them into the reference and resource sections.

(I’m particularly taken by WLRT and PHCS)

HPLP II

Walker SN, Sechrist KR, Pender NJ. The health-promoting lifestyle profile II. Omaha: University of Nebraska Medical Center, College Of Nursing; 1995; [Unpublished]
Includes links to the abstract, permisisons, the actual scale, scoring and usage.
Repository: http://deepblue.lib.umich.edu/handle/2027.42/85349

An example in a thesis:Appendix C p 105-106
http://www.dtic.mil/dtic/tr/fulltext/u2/a344505.pdf
WLRT

Predictors of success to weight-loss intervention program in individuals at high risk for type 2 diabetes. Diabetes Research & Clinical Practice, 01 November 2010, vol./is. 90/2(147-153), Kong W; Langlois MF; Kamga-Ngandé C; Gagnon C; Brown C; Baillargeon JP
Uses the 16-item weight-loss readiness tool (WLRT) – no reference
DAS3 (Diabetes attitude scale)

From:
Attitudes towards gestational diabetes among a multiethnic cohort in Australia, Journal of Clinical Nursing Sep 1 2010 Mary Carolan, Cheryl Steele and Heather Margetts

“The instrument used to measure attitudes was the DAS3, which has been shown to be a valid and reliable measure of diabetes-related attitudes (Anderson et al. 1998).”
Ref:
Anderson RM, Fitzgerald JT, Funnell MM & Gruppen LD (1998)
The third version of the diabetes attitude scale. Diabetes Care 21(9) , 1403–1407.
http://care.diabetesjournals.org/content/21/9/1403.full.pdf+html
The full DAS can be obtained from RM Anderson.
Health Value Scale and Generalized Self-Efficacy Scale

From:
Predictors of Health-Promoting Behaviors Among Freshman Dental Students at Istanbul University. Kadriye Peker, Ph.D. and Gülçin Bermek, Ph.D. Journal of Dental Education March 1, 2011 vol. 75 no. 3 413-420
http://www.jdentaled.org/content/75/3/413.long

“HPLP II was used to measure students’ health-promoting behaviors.26 The scale was comprised of fifty-two items in six subscales: spiritual growth, health responsibility, physical activity, nutrition, interpersonal relations, and stress management.”

“The four-item Health Value Scale was used to assess the value participants place on their health.28 Response categories ranged from 1 (strongly disagree) to 7 (strongly agree).”
28. Lau R, Hartman K, Ware J. Health as a value: methodological and theoretical considerations. Health Psychol 1986; 5(1):25–43.

“the Generalized Self-Efficacy Scale is designed to assess optimistic self-beliefs to cope with a variety of difficult demands in life29 “
29. Schwarzer, R., & Jerusalem, M. (1995). Generalized Self-Efficacy scale. In J. Weinman, S. Wright, & M. Johnston, Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 35-37). Windsor, UK: NFER-NELSON.
Perceived Health Competence Scale

Smith, M. S., Wallston, K. A., & Smith, C. A. (1995). The development and validation
of the perceived health competence scale. Health Education Research. 10(1), 51-
64.
Example in a thesis:Appendix D p 108
http://www.dtic.mil/dtic/tr/fulltext/u2/a344505.pdf
Health Beliefs Model

I have found several articles on the Health Belief model and questionnaires related to it but they mainly seem to be developed for the specific research rather than being a standard questionnaire. For example:

O’Connell JK, Price JH, Roberts SM, Jurs SG, McKinley R (1985) Utilizing the health belief model to predict dieting and exercising behavior of obese and nonobese adolescents.Health Education Quarterly, vol./is. 12/4(343-51), 0195-8402;0195-8402 (1985)
Abstract:
This study was undertaken to explain dieting and exercising behavior of obese and nonobese adolescents as measured by the elements of the Health Belief Model (HBM). An elicitation questionnaire was used to determine salient beliefs about dieting, exercising, and obesity for each of the major components of the HBM. The Health Belief Model questionnaire, developed from the elicited salient beliefs, contained items employed to measure attitudes towards obesity and exercise, knowledge of obesity and exercise, weight locus of control, and beliefs and evaluations about obesity and exercise. Discriminant analysis and stepwise discriminant analysis were employed in the data analysis of the 69 obese and 100 nonobese HBM respondents to determine the relative importance of the investigated factors in predicting obesity. It was found that benefits of dieting was the most powerful predictor of dieting behavior for the obese adolescents, whereas susceptibility to the causes of obesity best explained present dieting behavior of nonobese adolescents. Exercising behavior of obese teenagers was best explained by cues to exercising. No HBM variables were significant in predicting exercising behavior of nonobese adolescents.

 

 

MEDLINE BACKGROUND RELATED:

Pender N (1996). Health promotion in nursing practice (3rd ed.). Stanford, CT: Appleton and Lange. 2010 6th edition too.

Health-Promoting Lifestyle Profile Simple Scale (HPLP-S)
Used in: The predictors of adopting a health-promoting lifestyle among work
site adults with prediabetes , Journal of Clinical Nursing Oct 1st 2010
No Reference though.
http://web.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=4&sid=2f8304a3-f2c4-4a97-b795-57801e2851b1%40sessionmgr104&hid=122

Teng HL, Yen M, Fetzer S. Health promotion lifestyle profile-II: Chinese version short form. Journal of Clinical Nursing. 2010;66:1864–1873

Wierenga ME(1994) Life-style modification for weight control to improve diabetes health status. Patient education and counselling 23(1) p. 33-40
AB:
The purpose of this study was to describe the relationship among variables which are associated with life-style modification, knowledge of diabetes, social support, health practices, and body mass index, to examine their effect on health status, and to test the effectiveness of a community based life-style modification program for weight control. Adults (n = 66) with non-insulin-dependent diabetes mellitus participated in either a treatment or control group. The treatment consisted of 5 weekly 90-min sessions on modifying eating and exercise patterns. All participants completed a personal resource questionnaire (PRQ), health practices survey (HPS), and diabetes health status questionnaire (DHS) at intake, 5 weeks, and 4 months. Knowledge of diabetes was assessed only at intake. Knowledge of diabetes, social support, and health practices explained 27% of the variance in health status, but health practices explained the largest (18%) proportion of the variance and was the only study variable significantly affected by the life-style modification program
PM: PUBMED 7971538

 

Pathology Cost estimate

Pathology Costs

  • Glucose 1.29
  • HbA1c 3.44
  • ALT 1.29
  • Creatinine 1.29
  • Full Lipid Profile 2.58

This would equate to 9.89 per pt per test profile and estimates to 400 per surgery and 5k for the project anticipating one pre and one post intervention.

This is based on 50 letters sent per practice, 50% response initially, and 33% completing the course and having a second profile

Initial Costing Estimate

This is our initial “fag packet” calculation.

Eat4Health Course (10 * 1.5 hr Sessions plus taster) 400 * 15 courses

Hosting (10 * 1.5.hr Accommodation plus surgery staff) 400 * 15 courses

Mailing costs 500

Blood tests for those participating 10 pounds * 250 patients (initial costs plus repeat at completion)

Misc Admin Costs 3000

Public Health and CCG costs are assumed to be covered within budget

Total estimate 18000

 

First meeting

Today we held the inaugral meeting (2hrs) between the Clinical Lead (TW), CCG Lead (AT), and Public Health Lead (BR).

We discussed the overall plan, and outlined costing.  We aim to mail out to practices shortly, gaining their general permission to enrol them into the study, identify their situation with respect to clinical system, and whether they are able to host the intervention planned.

As previously agreed we have chosen QDiabetes as the tool to identify and stratify Diabetic Risk and Eat4Health as our intervention.

We discussed blood tests required.  We aim to collect HbA1c, ALT, Fasting lipid profile, fasting blood glucose for each patient entering the intervention.  We need to discuss with the lab to cost out the blood test bundle and how to facilitate blood requests to reduce impact for practices.  We use SunQuest ICE online pathology requesting and this doesn’t currently have the facility to “bulk request” lab tests.

In order to understand the pre-intervention position of the sample, we need to assess both objective values (blood tests and body morphology) and also subjective attitudes, to exercise, food and lifestyle etc.  BR charged to identify validated lifestyle questionnaires.

We agreed that when the target group is identified they will be invited to a “taster” session where the concept will be discussed and patients invited to sign up for the full intervention.

We felt that surgeries may have different facilities and needs for hosting Eat4Health sessions, so a mixture of surgery based and non-surgery based sessions will be offered.  Include in initial Questionnaire to practices.

We need to formulate a full detailed business case for the CCG to sign off the budget that has been agreed

Outcomes of First Meeting

TW – Generate information, consent and signup forms for practices

TW – Collate timescales from practices

AT – Liaise with Path Lab

BR – Identify lifestyle questionnaires

BR – Identify cost of Eat4Health courses and taster sessions

BR/TW – To liaise regarding Detailed Business Case