Publications (TPG get posts plugin)

  • Personal Wellbeing Score (PWS)

    Benson T, Sladen J, Liles A, Potts H. Personal Wellbeing Score (PWS) – a short version of ONS4, testing and validation in social prescribing. Research Paper 17/03, October 2017

    Aims

    Wellbeing is a key policy objective in health and care services.  Our aim was to develop a short generic measure of personal (subjective) wellbeing for routine use as a performance measure in patient-centred care and healthcare quality improvement alongside other patient-reported outcome and experience measures.

    Methods

    The Personal Wellbeing Score (PWS) is a patient-reported outcome measure, based on the Office of National Statistics (ONS) four personal well-being questions (ONS4) and thresholds. PWS has the same look and feel as other measures in the R-Outcomes family of surveys.  Word length and reading age were compared with eight other measures.  Anonymous data from five social prescribing projects, were analysed. Internal structure was examined using distributions, intra-item correlations, Cronbach’s alpha and exploratory factor analysis. Construct validity was assessed using hypothesised associations with health status, health confidence, patient experience, age, gender and medications. Scores on referral and after referral assessed responsiveness.

    Results

    Differences from ONS4 include brevity, fewer response options, positive wording and a summary score. PWS is short (42 words) with low reading age (9 years).

    In this population (1,299 respondents, 60% female, average age 81 years), missing values were less than 2%. PWS showed good internal reliability (Cronbach’s alpha = 0.90). Exploratory factor analysis suggested that the four PWS items relate to a single dimension. PWS summary scores correlate positively with health confidence (r=0.60), health status (r=0.58), patient experience (r=0.30) and age group (r=0.24). PWS is responsive to social prescribing intervention.

    Conclusions

    The Personal Wellbeing Score (PWS) is a short variant of ONS4. It is easy to use with good psychometric properties, suitable for routine use in quality improvement and health services research.

    PWS paper 1710

  • Health Confidence Score (HCS) – development and validation

    Benson T, Bowman C, Sladen J, Liles A, Potts HWW. Health Confidence Score (HCS) – Development and Validation. Research Paper 17/02 September 2017

    Understanding how confident patients are in looking after their own health is essential to improve patient outcomes and clinical support. With few suitable tools available to measure self-care health confidence, we developed and validated a short, generic survey instrument.

    The Health Confidence Score (HCS) was developed through literature review, patient and expert focus groups and discussions, before being further validated over a 3-year period.

    This report covers results of two studies testing construct and concurrent validity: an online survey (n=1031, study1), and a face-to-face survey (n=378, study2). Scores were correlated against the My Health Confidence (MHC) rating scale, howRu (health status) and relevant demographics.

    The Health Confidence Score is short (50 words) with good readability (reading age 8). Items are reported independently and as a summary score. The HCS has four items covering health knowledge, ability to self-manage, access to help and shared decision-making, each having four response options.

    In study2, the mean summary score was 76.7 (SD 20.4) on 0-100 scale. Cronbach’s alpha = 0.82. Exploratory factor analysis suggests that the four items relate to a single dimension. Correlation of the HCS summary score with MHC was high (Spearman r=0.76). It was also associated with health status (Spearman r=0.49), but negatively with number of medications taken (r=–0.29) and age (r=–0.22). It was not associated significantly with ethnicity, having children or education level.

    The Health Confidence Score is short, easy to use, with good psychometric properties and construct validity. The summary score gives an overall picture of confidence and each item is meaningful independently. It can be integrated into electronic records.

    HCS paper 1709

  • Evaluation of a new short generic measure of HRQoL: howRu

    Aims

    Quality of life is paramount for patients and clinicians, but existing measures of health were not developed for routine use. This paper describes the development and testing of a new generic tool for measuring health related quality of life (HRQoL) with direct comparison to the SF-12 Health Survey.

    Methods

    The new tool (howRu) has four items (discomfort, distress, disability and dependence), rated using four levels (none, a little, quite a lot and extreme), providing 256 possible states (44 ); it has an aggregate scoring scheme with a range from 0 (worst) to 12 (best). Psychometric properties were examined in a telephone survey, which also recorded SF-12.

    Results

    The howRu script is shorter than SF-12 (45 words vs 294 words) and has better readability statistics. 2751 subjects, all with long-term conditions (average age 62, female 62.8%), completed the survey; 21.7% were at the ceiling (no reported problems on any dimension); 0.9% at the floor. Inter-item correlations, Cronbach’s alpha and principal factor analysis suggest that a single summary score is appropriate. Correlations between the physical and mental components of both howRu and SF-12 were as expected. Across all patients the howRu score was correlated with PCS-12 (r=0.74), MCS-12 (r=0.49) and the sum of PCS12 and MCS-12 (r=0.81). Subjects were classified by howRu score, primary condition, the number of conditions suffered, age group, duration of illness and area of residence. Across all six classi- fications, the correlation of the mean howRu score with the mean PCS-12 for each class was r=0.91, with MCS-12, r=0.45 and with the sum of PCS-12 and MCS-12, r=0.97.

    Conclusions

    HowRu is a new short generic measure of HRQoL, with good psychometric properties. It generates similar aggregate results to SF-12. It could provide a quick and easy way for practitioners to monitor the health of patients with long-term conditions.

    Benson T, Whatling J, Arikan S, Sizmur S, McDonald D, Ingram D: Evaluation of a new short generic measure of HRQoL: howRu. Informatics in Primary Care 2010; 18:89-101.

    https://hijournal.bcs.org/index.php/jhi/article/view/758/770


     

  • Comparison of howRu and EQ-5D measures of health-related quality of life in an outpatient clinic

    This paper reports on a head-to-head study of howRu and EQ-5D on patients with cardiovascular disease. HowRu is a short generic measure of health-related quality of life comprising 39 words, designed for routine use, which we compare with EQ-5D (230 words). Patients attending a clinic completed both instruments. Completed data were available for 116 patients, 51% female, mean age 56 and SD 20. HowRu is shorter, has better readability statistics, a higher completion rate, a wider range of states used and a smaller ceiling effect than EQ-5D. The correlations of howRu with EQ-5D are similar to those of EQ-5D with other validated instruments.

    Benson T, Potts H, Whatling J, Patterson D. Comparison of howRu and EQ-5D measures of health-related quality of life in an outpatient clinic. Informatics in Primary Care 2013; 21 (1) 12-17.

    http://hijournal.bcs.org/index.php/jhi/article/view/9


     

  • Performance of EQ-5D, howRu and Oxford hip & knee scores in assessing the outcome of hip and knee replacements

    This compares the outcomes of hip and knee replacement surgery as measured by howRu (n=116) and EQ-5D (n=59,036), with both cohorts using the Oxford Hip and Knee scores as a common comparator. Outcome is the difference between the score prior to surgery and six months after surgery. For hip replacement, the correlation of outcome between howRu and Oxford hip score was r=0.77, for EQ-5D r=0.64. For knee replacement, the correlation of outcome between howRu and Oxford knee score was r=0.86, for EQ-5D r=0.59.

    Benson T, Williams DH, Potts HWW. Performance of EQ-5D, howRu and Oxford hip & knee scores in assessing the outcome of hip and knee replacements. BMC Health Services Research 2016; 16:512

    http://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-016-1759-x


     

  • A short generic patient experience questionnaire: howRwe development and validation

    This describes the development and purposes of the howRwe patient experience measure. It includes a case study of a service improvement project at Oxford University Hospitals, showing statistically significant improvements in promptness and organisation aspects, after the change, with no change in clinical compassion or communication aspects.

    Benson T, Potts HWW. A short generic patient experience questionnaire: howRwe development and validation. BMC Health Services Research 2014, 14:499.

    http://www.biomedcentral.com/1472-6963/14/499


     

  • Validation of the howRu and howRwe questionnaires at the individual patient level.

    This presents the results of a Dutch study which explored how well the howRu and howRwe surveys capture patients’ perceptions. Patients competed the surveys and were then interviewed by a review panel of GPs, who then completed the same surveys. The correlation between the patients and the review panel for howRu was CCC=0.80; for howRwe CCC=0.57. They concluded that howRu is suitable for use clinically at the individual patient level, but howRwe is not [NB howRwe is only used as an aggregate measure].

    Hendriks SH, Rutgers J, van Dijk PR, Groenier KH, Bilo H, Kleefstra N, Kocks JWH, van Hateren KJJ, Blanker MH. ​Validation of the howRu and howRwe questionnaires at the individual patient level. BMC Health Services Research 2015, 15:447.

    http://www.biomedcentral.com/1472-6963/15/447


     

  • Health-Related Quality of Life and Patient Experience in Care Homes

    This describes the methods used and results from in 360 care homes in UK, Australia and New Zealand. Staff reported on 19,202 residents; 10,327 residents self-reported as well as 6,966 visitors. Residents with higher howRu scores reported better experience too. This demonstrated the practicality of measuring health status and experience at scale in care homes. The measures are simple to use, yet provide results that clearly discriminate between different homes, groups of residents and their experience.

    Benson T, Bowman C. Health-Related Quality of Life and Patient Experience in Care Homes: A study in Three Countries. Medicine 2.0 London, Sept 2013.

    http://www.medicine20congress.com/ocs/index.php/med/med2013/paper/view/1365


     

  • Development and validation of a short health confidence score

    The first description of the Health Confidence Score, which is short, quick and easy to use. It includes results from two surveys (n=1031 and n=378). Correlation with the My Health Confidence scale is r=0.76. Other results were as predicted. People in poorer health tend to be less confident in their ability to deal with it.

    Benson T, Potts HWW, Bowman C. Development and validation of a short health confidence score. Value in Health 2016; 19 (3) A94.

    http://www.valueinhealthjournal.com/article/S1098-3015(16)01810-6/fulltext


     

  • PROMs: Application of sentic computing to the development of a novel unified framework for measuring health-care quality.

    This summarises the development of howRu and suggests how a holistic approach to natural language understanding (sentic computing) can supplement tools such as howRu in measuring quality and outcomes in health-care.

    Cambria E, Benson T, Eckl C, Hussain A. Sentic PROMs: Application of sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Systems with Applications 2012 39: 10533–10543.

    http://sentic.net/sentic-proms.pdf


     

  • The Load Model: An alternative to QALY

    QALYs (Quality Adjusted Life Years) are widely used in economic evaluation in health care, but have been criticised. The Load Model is an alternative model. Load is the average annual weight attributed to morbidity and mortality over a defined period, using weightings based on preference judgements. In a worked example, the morbidity component is higher in the Load model than in the QALY model, based on otherwise identical assumptions. When comparing alternative outcomes, there are also large differences between the two models. Given the role of the QALYs in economic evaluation, the implications of an alternative, which generates such different results, warrants further exploration.

    Benson T. The Load Model: An alternative to QALY. Journal of Medical Economics 2017; 20 (2): 107-113.

    http://www.tandfonline.com/doi/abs/10.1080/13696998.2016.1229198


     

  • The history of the Read codes: the inaugural James Read Memorial Lecture 2011

    This describes the origins and evolution of the Read Codes, which have been used by all GPs in UK and New Zealand. The Read Codes are one of the foundations of SNOMED CT.

    Benson T. The history of the Read codes: the inaugural James Read Memorial Lecture 2011. Journal of Innovation in Health Informatics 2011; 19(3): 173-82

    https://hijournal.bcs.org/index.php/jhi/article/view/811


     

  • Why general practitioners use computers and hospital doctors do not

    Almost all British general practitioners use computers in their consulting rooms, but most hospital doctors do not. Over 30 years, leaders of the GP profession worked with government to provide incentives for computerising practices and to remove barriers. In hospitals computing was treated as a management overhead, and doctors had no incentives to become involved. The success of the government’s plans for “joined up” computer based health services depends on providing appropriate incentives to hospital doctors. GP computerisation has been a success, but what works in a GP surgery does not readily scale up to work in a hospital. Computer-based patient records have a more diverse range of uses in hospitals than in general practice, and simple unidimensional classification schemes such as the original Read codes cannot cope. In hospitals, many different computer systems need to be linked together, requiring common interoperability standards. Protection of privacy is a greater problem in hospitals, and the number of potential users makes greater demands on hardware and networks.

    Benson T. Why general practitioners use computers and hospital doctors do not—Part 1: incentives and Part 2: scalability. BMJ 2002; 325: 1086-1093

    http://www.bmj.com/content/325/7372/1086


     

  • Principles of Health Interoperability: SNOMED CT, HL7 and FHIR. 3rd Edition, Springer 2016

    Healthcare interoperability delivers information when and where it is needed. Everybody stands to gain from safer more soundly based decisions and less duplication, delays, waste and errors. This book introduces healthcare interoperability and the main standards used.

    The third edition includes a new part on FHIR (Fast Health Interoperability Resources), the most important new health interoperability standard for a generation. FHIR combines the best features of HL7’s v2, v3 and CDA while leveraging the latest web standards and a tight focus on implementation. FHIR can be implemented at a fraction of the price of existing alternatives and is well suited for mobile phone apps, cloud communications and EHRs.

    The book is organized into four parts. The first part covers the principles of health interoperability, why it matters, why it is hard and why models are an important part of the solution. The second part covers clinical terminology and SNOMED CT. The third part covers the main HL7 standards: v2, v3, CDA and IHE XDS. The new fourth part covers FHIR and has been contributed by Grahame Grieve, the original FHIR chief.

    Benson T, Grieve G. Principles of Health Interoperability: SNOMED CT, HL7 and FHIR. 3rd Edition, Springer 2016