How are we evaluating the impact of new care models on how people feel in Wessex
Darnton P, Liles A, Matheson-Monnet C, Sibley A. How are we evaluating the impact of new care models on how people feel in Wessex. Research paper, November 2017
Report of the development and validation of the Personal Wellbeing Score
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
Report of the development and validation of the Health Confidence Score
Benson T, Bowman C, Sladen J, Liles A, Potts HWW. Health Confidence Score (HCS) – Development and Validation. Research Paper 17/02 September 2017
This paper introduces eight different services providing social prescribing in Wessex and describes our work to understand and evaluate the impact they are having, to support their development.
Liles A, Darnton P. Social Prescribing in Wessex: Understanding its impact and supporting spread. Wessex AHSN May 2017.
The first publication on howRu, describing why and how it was developed for routine use. It includes results from 2,750 people with long term conditions. The correlation of summary scores for howRu and the SF-12 health survey was high (r=0.81).
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.
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.
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.
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. The correlation of outcome between howRu and the Oxford scores was higher than that of EQ-5D (Hips howRu r=0.77, EQ-5D r=0.64; Knees howRu r=0.86, 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
This summarises the results of a direct comparison of howRu and EQ-5D in 116 patients at a cardiovascular out-patient clinic. howRu is shorter than EQ-5D, with better readability statistics, a higher completion rate, a wider range of states used and a smaller ceiling effect. The correlation between howRu and EQ-5D (r=0.70) is similar to those of other validated instruments with EQ-5D.
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.
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.
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.
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.
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.
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
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
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.
Foreword by John Halamka
PART 1 Principles of Health Interoperability
Chapter 1 The Health Information Revolution
Chapter 2 Why Interoperability is Hard
Chapter 3 Models
Chapter 4 UML, XML and JSON
Chapter 5 Information Governance
Chapter 6 Standards Development Organizations
PART 2 Terminology and SNOMED CT
Chapter 7 Clinical Terminology
Chapter 8 Coding and Classification Schemes
Chapter 9 SNOMED CT
Chapter 10 SNOMED CT Concept Model
Chapter 11 Implementing SNOMED CT
PART 3 HL7 and Interchange Formats
Chapter 12 HL7 Version 2
Chapter 13 The HL7 V3 RIM
Chapter 14 Constrained Information Models
Chapter 15 CDA – Clinical Document Architecture
Chapter 16 HL7 Dynamic Model
Chapter 17 Sharing Documents and IHE XDS
PART 4 Fast Healthcare Interoperability Resources (FHIR)
Chapter 18 Principles of FHIR
Chapter 19 The FHIR RESTful API
Chapter 20 FHIR Resources
Chapter 21 Conformance and Terminology
Chapter 22 Implementing FHIR
Benson T, Grieve G. Principles of Health Interoperability: SNOMED CT, HL7 and FHIR. 3rd Edition, Springer 2016