To identify how social return on investment (SROI) analysis—traditionally used by business consultants—has been interpreted, used and innovated by academics in the health and social care sector and to assess the quality of peer-reviewed SROI studies in this sector.
Community and residential settings.
A wide range of demographic groups and age groups.
The following databases were searched: Web of Science, Scopus, CINAHL, Econlit, Medline, PsychINFO, Embase, Emerald, Social Care Online and the National Institute for Health and Care Excellence. Limited uptake of SROI methodology by academics was found in the health and social care sector. From 868 papers screened, 8 studies met the criteria for inclusion in this systematic review. Study quality was found to be highly variable, ranging from 38% to 90% based on scores from a purpose-designed quality assessment tool. In general, relatively high consistency and clarity was observed in the reporting of the research question, reasons for using this methodology and justifying the need for the study. However, weaknesses were observed in other areas including justifying stakeholders, reporting sample sizes, undertaking sensitivity analysis and reporting unexpected or negative outcomes. Most papers cited links to additional materials to aid in reporting. There was little evidence that academics had innovated or advanced the methodology beyond that outlined in a much-cited SROI guide.
Academics have thus far been slow to adopt SROI methodology in the evaluation of health and social care interventions, and there is little evidence of innovation and development of the methodology. The word count requirements of peer-reviewed journals may make it difficult for authors to be fully transparent about the details of their studies, potentially impacting the quality of reporting in those studies published in these journals.
Examination of the prevalence and patterns of multimorbidity among the elderly in China.
More than 10 000 households in 28 of the 34 provinces of mainland China.
11 707 Chinese adults aged 60 and over.
Prevalence and patterns of multimorbidity among the participants. Relative risks were calculated to estimate the probability of up to 14 chronic conditions coexisting with each other. Observed-to-expected (O/E) ratios were used to analyse the patterns of multimorbidity.
Multimorbidity was present in 43.6% of respondents from the sample population, with women having the greater prevalence compared with men. There were 804 different comorbidity combinations identified, including 76 dyad combinations and 169 triad combinations. The top 10 morbidity dyads and triads accounted for 69.01% and 47.05% of the total dyad and triad combinations observed, respectively. Among the 14 chronic conditions included in the study, asthma, stroke, heart attack and six other chronic conditions were the main components of multimorbidity due to their high relative risk ratios. The most frequently occurring clusters with higher O/E ratios were stroke along with emotional, nervous, or psychiatric problems; memory-related diseases together emotional, nervous, or psychiatric problems; and memory-related diseases and asthma accompanied by chronic lung diseases and asthma.
The results of this study highlight the high prevalence of multimorbidity in the elderly population in China. Further studies are required to understand the aetiology of multimorbidity, and future primary healthcare policies should be made while taking multimorbidity into consideration.
Drug–drug interaction (DDI) alerts in hospital electronic medication management (EMM) systems are generated at the point of prescribing to warn doctors about potential interactions in their patients’ medication orders. This project aims to determine the impact of DDI alerts on DDI rates and on patient harm in the inpatient setting. It also aims to identify barriers and facilitators to optimal use of alerts, quantify the alert burden posed to prescribers with implementation of DDI alerts and to develop algorithms to improve the specificity of DDI alerting systems.
A controlled pre-post design will be used. Study sites include six major referral hospitals in two Australian states, New South Wales and Queensland. Three hospitals will act as control sites and will implement an EMM system without DDI alerts, and three as intervention sites with DDI alerts. The medical records of 280 patients admitted in the 6 months prior to and 6 months following implementation of the EMM system at each site (total 3360 patients) will be retrospectively reviewed by study pharmacists to identify potential DDIs, clinically relevant DDIs and associated patient harm. To identify barriers and facilitators to optimal use of alerts, 10–15 doctors working at each intervention hospital will take part in observations and interviews. Non-identifiable DDI alert data will be extracted from EMM systems 6–12 months after system implementation in order to quantify alert burden on prescribers. Finally, data collected from chart review and EMM systems will be linked with clinically relevant DDIs to inform the development of algorithms to trigger only clinically relevant DDI alerts in EMM systems.
This research was approved by the Hunter New England Human Research Ethics Committee (18/02/21/4.07). Study results will be published in peer-reviewed journals and presented at local and international conferences and workshops.
The aim of this study was to examine long-term trends in the receipt of medicines information (MI) among adult medicine users from 1999 to 2014.
Repeated cross-sectional postal survey from the years 1999, 2002, 2005 and 2008–2014.
Each study year, a new nationally representative sample of 5000 Finns aged 15–64 years was drawn from the Population Register Centre of Finland.
The range of annual respondents varied from 2545 to 3371 and response rates from 53% to 67%. Of the total responses (n=29 465), 64% were from medicine users (n=18 862, ranging by year from 58% to 68%).
Receipt of information on medicines in use within 12 months prior to the survey from a given list of consumer MI sources available in Finland.
Physicians, community pharmacists and package leaflets were the most common MI sources throughout the study period. Receipt of MI increased most from the Internet (from 1% in 1999 to 16% in 2014), while decreased most from physicians (62% to 47%) and package leaflets (44% to 34%), and remained stable from community pharmacists (46% to 45%) and nurses (14% to 14%). In 1999, of the medicine users 4% did not report receipt of MI from any of the sources listed in the survey, while this proportion had remarkably increased to 28% in 2014.
Healthcare professionals and package leaflets had still a dominating importance in 2014 despite the growing number of MI sources over time, but still a minority of adult medicine users reported receiving MI via the Internet in 2014. Worrying is that the proportion of adult medicine users who did not receive MI from any of the sources became seven fold during the study period.
The ‘learning healthcare system’ (LHS) has been proposed to deliver better outcomes for patients and communities by analysing routinely captured health information and feeding back results to clinical staff. This approach is being piloted in the Connected Health Cities (CHC) programme in four regions in the north of England. This article describes the protocol of the evaluation of this programme.
In designing this evaluation, we had to take a pragmatic approach to ensure the feasibility of completing the work within 1 year. Furthermore, we have designed the evaluation in such a way as to be able to capture differences in how each of the CHC regions uses a variety of methods to create their own LHS. A mixed methods approach has been adopted for this evaluation due the scale and complexities of the pilot study. A documentary review will identify how CHC pilot study deliverables were operationalised. To gain a broad understanding of CHC staff experiences, an online survey will be offered to all staff to complete. Semi-structured interviews with key programme staff will be used to gain a deeper understanding of key achievements, as well as how challenges have been overcome or managed. Our data analysis will triangulate the documentary review, survey and interview data. A thematic analysis using our logic model as a framework will also be used to assess progress against the CHC programme deliverables and to identify recommendations to support future programme decision-making.
Ethical approval was granted by The University of Manchester Ethics Committee on 24 May 2018. The results will be actively disseminated through peer-reviewed journals, conference presentations, social media, the internet and various stakeholder/patient and public engagement activities.
Multimorbidity refers to the presence of two or more chronic health conditions within one person, where no one condition is primary. Research suggests that multimorbidity is highly correlated with chronic pain, which is pain lasting longer than 3 months. Psychotherapeutic interventions for people living with chronic illness have resulted in reduced symptom reporting and improved psychological well-being. There is a dearth of research, however, using online psychotherapy for people living with multimorbidity where chronic pain is a central condition. This study will compare the effectiveness of an online acceptance and commitment therapy (ACT) intervention with a waiting list control condition in terms of improving health-related quality of life (HRQoL) and reducing levels of pain interference in people with chronic pain and at least one other condition.
192 adult participants with non-malignant pain that persists for at least 3 months and at least one other medically diagnosed condition will be randomised to one of two study conditions. The experimental group will undergo an eight-session internet-delivered ACT programme over an 8-week period. A waiting list group will be offered the ACT intervention after the 3-month follow-up period. HRQoL and pain interference will act as the primary outcomes. Data will be analysed using a linear mixed model and adjusted to account for demographic and clinical variables as necessary. A Study Within a Trial will be incorporated to examine the effect on recruitment and retention of showing participants an animated educational video.
Ethical approval has been granted by the Research Ethics Committee of the National University of Ireland, Galway. Dissemination of results will be via peer reviewed journal articles and conference presentations.
To determine the prevalence, degree of trust and usefulness of the online health information seeking source and identify associated factors in the adult population from the rural region of China.
A cross-sectional population-based study.
A self-designed questionnaire study was conducted between May and June 2015 in four districts of Zhejiang Province.
652 adults aged ≥18 years (response rate: 82.8%).
The prevalence, degree of trust and usefulness of online health information was the primary outcome. The associated factors were investigated by 2 test.
Only 34.8% of participants had faith in online health information; they still tended to select and trust a doctor which is the first choice for sources of health information. 36.7% of participants, being called ‘Internet users’, indicated that they had ever used the internet during the last 1 year. Among 239 internet users, 40.6% of them reported having sought health information via the internet. And 103 internet users responded that online health information was useful. Inferential analysis demonstrated that younger adults, individuals with higher education, people with a service-based tertiary industry career and excellent health status used online health information more often and had more faith in it (p
Using the internet to access health information is uncommon in the rural residential adult population in Zhejiang, China. They still tend to seek and trust health information from a doctor. Internet as a source of health information should be encouraged.
Health systems in North America and Europe have been criticised for their lack of safety, efficiency and effectiveness despite rising healthcare costs. In response, healthcare leaders and researchers have articulated the need to transform current health systems into continuously and rapidly learning health systems (LHSs). While digital technology has been envisioned as providing the transformational power for LHSs by generating timely evidence and supporting best care practices, it remains to be ascertained if it is indeed playing this role in current LHS initiatives. This paper presents a protocol for a scoping review that aims at providing a comprehensive understanding of how and to what extent digital technology is used within LHSs. Results will help to identify gaps in the literature as a means to guide future research on this topic.
Multiple databases and grey literature will be searched with terms related to learning health systems. Records selection will be done in duplicate by two reviewers applying pre-defined inclusion and exclusion criteria. Data extraction from selected records will be done by two reviewers using a piloted data charting form. Results will be synthesised through a descriptive numerical summary and a mapping of digital technology use onto types of LHSs and phases of learning within LHSs.
Ethical approval is not required for this scoping review. Preliminary results will be shared with stakeholders to account for their perspectives when drawing conclusions. Final results will be disseminated through presentations at relevant conferences and publications in peer-reviewed journals.
Digital data generated in the course of clinical care are increasingly being leveraged for a wide range of secondary purposes. Researchers need to develop governance policies that can assure the public that their information is being used responsibly. Our aim was to develop a generalisable model for governance of research emanating from health data repositories that will invoke the trust of the patients and the healthcare professionals whose data are being accessed for health research. We developed our governance principles and processes through literature review and iterative consultation with key actors in the research network including: a data governance working group, the lead investigators and patient advisors. We then recruited persons to participate in the governing and advisory bodies. Our governance process is informed by eight principles: (1) transparency; (2) accountability; (3) follow rule of law; (4) integrity; (5) participation and inclusiveness; (6) impartiality and independence; (7) effectiveness, efficiency and responsiveness and (8) reflexivity and continuous quality improvement. We describe the rationale for these principles, as well as their connections to the subsequent policies and procedures we developed. We then describe the function of the Research Governing Committee, the majority of whom are either persons living with diabetes or physicians whose data are being used, and the patient and data provider advisory groups with whom they consult and communicate. In conclusion, we have developed a values-based information governance framework and process for Diabetes Action Canada that adds value over-and-above existing scientific and ethics review processes by adding a strong patient perspective and contextual integrity. This model is adaptable to other secure data repositories.
Asthma and chronic obstructive pulmonary disease (COPD) are common respiratory conditions, which result in significant morbidity worldwide. These conditions are associated with a range of non-specific symptoms, which in themselves are a target for health research. Such research is increasingly being conducted using electronic health records (EHRs), but computable phenotype definitions, in the form of code sets or code lists, are required to extract structured data from these large routine databases in a systematic and reproducible way. The aim of this protocol is to specify a systematic review to identify code sets for respiratory symptoms in EHRs research.
MEDLINE and Embase databases will be searched using terms relating to EHRs, respiratory symptoms and use of code sets. The search will cover all English-language studies in these databases between January 1990 and December 2017. Two reviewers will independently screen identified studies for inclusion, and key data will be extracted into a uniform table, facilitating cross-comparison of codes used. Disagreements between the reviewers will be adjudicated by a third reviewer. This protocol has been produced in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol guidelines.
As a review of previously published studies, no ethical approval is required. The results of this review will be submitted to a peer-reviewed journal for publication and can be used in future research into respiratory symptoms that uses electronic healthcare databases.
To introduce serialised medicines into an operational hospital dispensary and assess the technical effectiveness of digital medicine authentication (MA) technology under European Union Falsified Medicines Directive (EU FMD) conditions.
Thirty medicine lines were serialised using 2D data matrix labels and introduced into an operational UK National Health Service (NHS) hospital dispensary. Staff were asked to check medicines for two-dimensional (2D) data matrices and scan those products, in addition to their usual medicine preparation and checking processes. Four per cent of the study medicines were labelled with a 2D barcode which generated a pop-up, identifying the medicine as either authenticated elsewhere (falsified), authenticated here, expired or recalled.
An NHS teaching hospital based in the UK, the same site as the Naughton et al 2016 study.
General Pharmaceutical Council registered, accredited accuracy checking technicians and pharmacists.
Average response times, offline issues, instances of incorrect quarantine and workarounds. The EU FMD maximum response time is 300 milliseconds (ms).
During the checking stage of medicine preparation, the average response time for MA in this study was 131 ms. However, 4.67% of attempted authentications experienced offline issues, an increase of 4.23% from the previous study. An increase in offline instances existed alongside an increase in incorrect quarantine.
Digital drug screening has the capability of operating with average response times which are below the maximum EU FMD limit of 300 ms. However, there was an increased incidence of offline errors and cases of incorrect quarantine. The practical and legal implications of supplying a substandard or falsified medicine during offline periods without prior authentication or withholding supply until online status resumes are not yet fully understood.
Patient records are often fragmented across organisations and departments in UK health and care services, often due to substandard information technology. However, although government policy in the UK and internationally is strongly pushing ‘digital transformation’, the evidence for the positive impact of electronic information systems on cost, quality and safety of healthcare is far from clear. In particular, the mechanisms by which information availability is translated into better decision-making are not well understood. We do not know when a full interorganisational record is more useful than a key information summary or an institutional record. In this paper, we describe our scoping review of how interorganisational electronic health records affect decision-making by hospital physicians and pharmacists.
This scoping review will follow the Arksey and O’Malley (2005) methodology. The review has adopted sociotechnical systems thinking and the notion of distributed cognition as its guiding conceptual models. The UK National Institute for Health and Care Excellence Healthcare Databases Advanced Search will be used, as it incorporates key sources including PubMed, Medline, Embase, HMIC and Health Business Elite. A hand search will be conducted using the reference lists of included studies to identify additional relevant articles. A two-part study selection process will be used: (1) a title and abstract review and (2) full text review. During the first step, two researchers separately will review the citations yielded from the search to determine eligibility based on the defined inclusion and exclusion criteria. Related articles will be included if they are empirical studies that address how interorganisational records affect decision-making by hospital physicians and pharmacists.
The results will be disseminated through stakeholder meetings, conference presentations and peer-reviewed publication. The data used are from publicly available secondary sources, so this study does not require ethical review.
The Centers for Disease Control and Prevention (CDC) spend significant time and resources to track influenza vaccination coverage each influenza season using national surveys. Emerging data from social media provide an alternative solution to surveillance at both national and local levels of influenza vaccination coverage in near real time.
This study aimed to characterise and analyse the vaccinated population from temporal, demographical and geographical perspectives using automatic classification of vaccination-related Twitter data.
In this cross-sectional study, we continuously collected tweets containing both influenza-related terms and vaccine-related terms covering four consecutive influenza seasons from 2013 to 2017. We created a machine learning classifier to identify relevant tweets, then evaluated the approach by comparing to data from the CDC’s FluVaxView. We limited our analysis to tweets geolocated within the USA.
We assessed 1 124 839 tweets. We found strong correlations of 0.799 between monthly Twitter estimates and CDC, with correlations as high as 0.950 in individual influenza seasons. We also found that our approach obtained geographical correlations of 0.387 at the US state level and 0.467 at the regional level. Finally, we found a higher level of influenza vaccine tweets among female users than male users, also consistent with the results of CDC surveys on vaccine uptake.
Significant correlations between Twitter data and CDC data show the potential of using social media for vaccination surveillance. Temporal variability is captured better than geographical and demographical variability. We discuss potential paths forward for leveraging this approach.
To explore the scope of the published literature on computer-tailoring, considering both the development and the evaluation aspects, with the aim of identifying and categorising main approaches and detecting research gaps, tendencies and trends.
Original researches from any country and healthcare setting.
Patients or health consumers with any health condition regardless of their specific characteristics.
A systematic scoping review was undertaken based on the York’s five-stage framework outlined by Arksey and O’Malley. Five leading databases were searched: PubMed, Scopus, Science Direct, EBSCO and IEEE for articles published between 1990 and 2017. Tailoring concept was investigated for three aspects: system design, information delivery and evaluation. Both quantitative (ie, frequencies) and qualitative (ie, theme analysis) methods have been used to synthesis the data.
After reviewing 1320 studies, 360 articles were identified for inclusion. Two main routes were identified in tailoring literature including public health research (64%) and computer science research (17%). The most common facets used for tailoring were sociodemographic (73 %), target behaviour status (59%) and psycho-behavioural determinants (56%), respectively. The analysis showed that only 13% of the studies described the tailoring algorithm they used, from which two approaches revealed: information retrieval (12%) and natural language generation (1%). The systematic mapping of the delivery channel indicated that nearly half of the articles used the web (57%) to deliver the tailored information; printout (19%) and email (10%) came next. Analysis of the evaluation approaches showed that nearly half of the articles (53%) used an outcome-based approach, 44% used process evaluation and 3% assessed cost-effectiveness.
This scoping review can inform researchers to identify the methodological approaches of computer tailoring. Improvements in reporting and conduct are imperative. Further research on tailoring methodology is warranted, and in particular, there is a need for a guideline to standardise reporting.
Self-management is widely promoted but less attention is focused on the work required from patients. To date, many individuals struggle to practise self-management. ‘Patient work’, a concept that examines the ‘work’ involved in self-management, is an approach to understanding the tasks, effort, time and context from patient perspective. The purpose of our study is to use a novel approach combining non-obstructive observations via digital devices with in-depth qualitative data about health behaviours and motivations, to capture the full range of patient work experienced by people with type 2 diabetes and chronic comorbidities. It aims to yield comprehensive insights about ‘what works’ in self-management, potentially extending to populations with other chronic health conditions.
This mixed-methods observational study involves a (1) prestudy interview and questionnaires, (2) a 24-hour period during which participants wear a camera and complete a time-use diary, and a (3) poststudy interview and study feedback. Adult participants living with type 2 diabetes with at least one chronic comorbidity will be recruited using purposive sampling to obtain a balanced gender ratio and of participants using insulin and those using only oral medication. Interviews will be analysed using thematic analysis. Data captured by digital devices, diaries and questionnaires will be used to analyse the duration, time, context and patterns of health-related behaviours.
The study was approved by the Macquarie University Human Research Ethics Committee for Medical Sciences (reference number 5201700718). Participants will carry a wallet-sized card that explains the purpose of the study to third parties, and can remove the camera at any stage. Before the poststudy interview begins, participants will view the camera images in private and can delete any images. Should any images be used in future publications or presentations, identifying features such as human faces and names will be obscured.
eHealth is critically important to build strong health systems, and accelerate the achievement of sustainable development goals, particularly universal health coverage. To support and strengthen the health system, the eHealth architecture needs to be formulated and established prior to the implementation and development of any national eHealth applications and services. The aim of this study is to design and validate a standard questionnaire to assess the current status of national eHealth architecture (NEHA) components.
This study will use a mixed-methods design consisting of four phases: (1) item generation through review of evidences and experts’ opinions, (2) face and content validity of the questionnaire, (3) determination of a range of possible scenarios for each item included in the questionnaire and (4) evaluation of reliability. This questionnaire is expected to generate critical and important information about the status of NEHA components that will be useful for monitoring, formulating, developing, implementing and evaluating NEHA. Our paper will contribute, we envisage, to establishment of a socio-technical basis on which governments and other relevant sectors can compare the policy interventions that boost the availability and utilisation of eHealth services within their settings.
The Ethics Committee for Research at the Tehran University of Medical Sciences approved the study protocol. We will obtain informed consent from each participant and collect data anonymously to maintain confidentiality. The translation of the findings into future policy planning will include the production of a series of peer-reviewed articles, presentation of the findings at relevant eHealth conferences and preparation of policy reports to the international organisations aiming to strengthen national capacity for better-informed eHealth architecture.
The rising popularity of social media, since their inception around 20 years ago, has been echoed in the growth of health-related research using data derived from them. This has created a demand for literature reviews to synthesise this emerging evidence base and inform future activities. Existing reviews tend to be narrow in scope, with limited consideration of the different types of data, analytical methods and ethical issues involved. There has also been a tendency for research to be siloed within different academic communities (eg, computer science, public health), hindering knowledge translation. To address these limitations, we will undertake a comprehensive scoping review, to systematically capture the broad corpus of published, health-related research based on social media data. Here, we present the review protocol and the pilot analyses used to inform it.
A version of Arksey and O’Malley’s five-stage scoping review framework will be followed: (1) identifying the research question; (2) identifying the relevant literature; (3) selecting the studies; (4) charting the data and (5) collating, summarising and reporting the results. To inform the search strategy, we developed an inclusive list of keyword combinations related to social media, health and relevant methodologies. The frequency and variability of terms were charted over time and cross referenced with significant events, such as the advent of Twitter. Five leading health, informatics, business and cross-disciplinary databases will be searched: PubMed, Scopus, Association of Computer Machinery, Institute of Electrical and Electronics Engineers and Applied Social Sciences Index and Abstracts, alongside the Google search engine. There will be no restriction by date.
The review focuses on published research in the public domain therefore no ethics approval is required. The completed review will be submitted for publication to a peer-reviewed, interdisciplinary open access journal, and conferences on public health and digital research.
In the UK, primary care is seen as the optimal context for delivering care to an ageing population with a growing number of long-term conditions. However, if it is to meet these demands effectively and efficiently, a more precise understanding of existing care processes is required to ensure their configuration is based on robust evidence. This need to understand and optimise organisational performance is not unique to healthcare, and in industries such as telecommunications or finance, a methodology known as ‘process mining’ has become an established and successful method to identify how an organisation can best deploy resources to meet the needs of its clients and customers. Here and for the first time in the UK, we will apply it to primary care settings to gain a greater understanding of how patients with two of the most common chronic conditions are managed.
The study will be conducted in three phases; first, we will apply process mining algorithms to the data held on the clinical management system of four practices of varying characteristics in the West Midlands to determine how each interacts with patients with hypertension or type 2 diabetes. Second, we will use traditional process mapping exercises at each practice to manually produce maps of care processes for the selected condition. Third, with the aid of staff and patients at each practice, we will compare and contrast the process models produced by process mining with the process maps produced via manual techniques, review differences and similarities between them and the relative importance of each. The first pilot study will be on hypertension and the second for patients diagnosed with type 2 diabetes.
Ethical approval has been provided by East Midlands–Leicester South Regional Ethics Committee (REC reference 18/EM/0284). Having refined the automated production of maps of care processes, we can explore pinch points and bottlenecks, process variants and unexpected behaviour, and make informed recommendations to improve the quality and efficiency of care. The results of this study will be submitted for publication in peer-reviewed journals.
Internet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China.
We identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016).
Search query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks.
Baidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention.