Data For Health Services Research Research Articles

Evidence of positive care effects by digital health apps-methodological challenges and approaches

Since 2020, digital health applications (DiGA) can be prescribed at the expense of the German statutory health insurance (SHI) system after undergoing an approval procedure by the Federal Institute for Drugs and Medical Devices (BfArM). DiGA can be approved provisionally for 1year (with the option of extension) or permanently. The latter is dependent on scientific evidence of apositive effect on care, which can be amedical benefit or apatient-relevant structural and procedural improvement in care. However, it is apparent that the investigation of DiGA in scientific studies is challenging, as they are often complex interventions whose success also includes user and prescriber factors. In addition, health services research data underpinning the benefits of DiGA are lacking to date. In the current article, methodological considerations for DiGA research are presented, and aselection of internal medicine DiGAs is used to critically discuss current research practice.

Chronic disease management improves survival but not hospital presentations: a target trial approach using linked data from the Australian Stroke Clinical Registry.

ObjectivesData linkage can provide sufficient breadth and size of data, to draw reliable estimates of effectiveness, at a population level using real-world data. We compared differences in survival and hospital presentations following stroke or transient ischaemic attack (TIA), based on whether a Medicare funded chronic disease management plan was claimed. ApproachA population-based, comparative effectiveness study of Victorian and Queensland Australian Stroke Clinical Registrants (January 2012-June 2015), using the emulated target trial approach, was performed. Chronic disease management items were identified from Medicare claims in the 6-18 months post-stroke (exposure period). Data on covariates for model adjustment were obtained from hospital, pharmacy and aged care datasets. Outcomes at 19-30 months post-stroke were determined using the national death registry and state hospital data. Cox regression, adjusted using propensity score methods with inverse probability treatment weights was used to determine the effect of receipt of chronic disease management claims on outcomes. ResultsOf 28,775 AuSCR registrants, 27,435 (95.3%) were linked across the Medicare, pharmacy and hospital datasets. Following exclusions, 11,574 registrants from 42 hospitals (42% female, median age 70 years, 27% TIA) were eligible for the study. Overall, 45% of participants received chronic disease management during the exposure period. After propensity score weighting, there was excellent balance between groups across 35 baseline variables (standardised differences <0.1). Receipt of chronic disease management (vs non-receipt) was associated with a 30% reduced hazard of death (adjusted Hazard Ratio [aHR]: 0.70, 95%CI: 0.57, 0.87) but a 17% increase in hospital presentations (aHR: 1.17, 95%CI: 1.12, 1.23). Variation was observed between planned (aHR: 1.21, 95%CI: 1.10, 1.33) and unplanned (aHR: 1.15, 95%CI: 1.07, 1.23) presentations. ConclusionWe provide an evaluation of the effectiveness of Medicare funded chronic disease management within “real world” healthcare provision, thereby demonstrating the value of linked population data in health services research. Further work is underway to examine causal mechanisms.

7.I. Workshop: Data for health services research: what can we learn from other countries?

High-quality, comprehensive, and timely data is essential for health services research. Increasingly, health services research requires using not only individual datasets but linked data, which use common key variables to bring together information from multiple data sources. This can include clinical data (e.g., results from clinical examination and diagnostic tests), routinely collected data on utilization (e.g., insurance claims) and further, partially unstructured data produced in daily life - as well as data from other areas (economic, environment, social). The need for linked data is reinforced by digital transformation in health care around the world and the availability of new data resources (‘big data') as well as the recent COVID-19 pandemic. We carried out an international comparison of data for health services research - both regarding databases with individual level data as well as those on a geographical or population level. We asked experts from more than ten countries to provide feedback on data linkage initiatives to determine the content of the data, how the data is collected, how the data is linked and cleaned, how the data is stored, and how the data is used for research. In some countries, data collected in the health system is largely fragmented and there is no unique identifier to facilitate linkage across datasets. In other countries, the government supports data linkage initiatives and provides summary data for all public data (down to the census) which can be used on request for projects that are in the public interest. The development - and possible linkage - of relevant databases is closely related to the organizational design of the health care system, especially the structure of service provision and the corresponding billing and reimbursement mechanisms. The accessibility of these databases is also influenced by data protection regulations and standards, including cultural considerations. The objective of this research is to identify important and potentially useful case studies that could provide insights into the different approaches to data linkage that exist worldwide and to perhaps provide ideas for future more or less ‘disruptive' solutions to countries undertaking the ambitious endeavor of health data linking. Therefore, we designed the analysis in such a way that findings on data structure and flow in selected countries are, to the extent possible, system-independent and transferable, while simultaneously also capturing some country-specific regulatory frameworks. This workshop will explore how countries govern and use data generated in the health system and what additional insights about best practices can be learned from the international experience. The workshop will begin with a presentation about the analytical framework, followed by a panel discussion of the experience from country case studies, continue with an audience contribution, and wrap up with a conclusions round.Key messages Data linkage initiatives that bring together information from clinical, utilization, social, and other data sources are increasingly important in health services research.An international comparison of data linkage initiatives provides best practices for data linkage that could be applied in individual country settings.

An analytical framework for assessing data for health services research

Abstract This presentation will provide an overview of the conceptual framework we used as a basis for the analysis of the case studies. The framework distinguishes between data sets with care-relevant data i) at the individual level (i.e. microdata) and ii) at the non-individual level, and iii) from three or four large content blocks (health data, health care data, socio-demographic or economic data, environmental data). Using health data as an example, individual-level health data includes individual patient data, such as laboratory and clinical results, vital signs (body temperature, pulse rate, and respiration date), as well as diagnoses and health behavior. Non-individual level data includes aggregated data in areas such as life expectancy, years of life lost (YLL), years lost to disability (YLD), disability-adjusted life years (DALY), as well as population characteristics such as prevalence of risk factors and chronic illness. The framework we have developed shows linking possibilities that are available by either storing the data in common databases (e.g., based on an electronic health record) or by linking them via a unique personal characteristic (e.g., patient identifier). The country case studies selected in the research - largely within the European region but also Australia, Canada, the Republic of Korea, New Zealand, and the United States - are all evaluated using the same conceptual framework.

Using population register data for health services research – the Finnish experience

Abstract The personal identification number was introduced in Finland in the mid-1960s. By 1970, it was universally used in administrative databases including population census, causes of death, cancer, hospital discharge, and health insurance registries. Early on data protection regulations recognised the use of registries and ID numbers for research and register linkages which allowed expanding the use of register data in population health and health services research. The possibility to analyse electronic health records (eHRs) linked to individual whole-population sociodemographic data has allowed the use of a wide range of study designs from cross-sectional to longitudinal studies and to various hierarchical designs. Some registers extend follow-up time up to over 50 years with detailed mapping of individual event histories. Data also allow detailed disentangling of individual and contextual factors, such as socioeconomics and comorbidities vs. provider characteristics. While some data items draw on administrative decisions or clinical discretion, such as granting insurance benefits or decisions on surgery, the research use of the data requires understanding of processes used to construct data items. The use of population registers in health services research is clearly cost-effective. However, the lack of a high-performance computing capacity and environment suitable for processing large sensitive data, as well as inadequate information on the use of primary health care and eHRs has limited efficient use of extensive and complex linkage schemes and the utilisation of machine learning. The Finnish register authorities have improved computing facilities for research use and the legislative reform on secondary use of health care data is opening eHRs for research use granting higher granularity in describing content and quality of care. These developments enable big-data methods to be an essential part of the future methodological toolbox for health service research.

Expressions of Moral Values in Palliative Care Patients and Their Influence on End‐of‐Life Decisions

Optimizing end‐of‐life (EOL) care involves integrating decisions at various levels for patients and palliative care (PC) physicians. Measuring “quality” in PC delivery is therefore challenging and often not assessed. One focus of quality is to measure features of communication that help us understand patients’ personal values and beliefs that influence choices fostering patient‐centered treatment. These can be derived from the vocabulary patients use, and we can use this knowledge to differentiate and respond more accurately to the needs of patients. The objective of this study was to relate deep moral values to concrete moral actions, and we mine a large‐scale, human‐generated dataset to investigate morally relevant phenomena in a high‐stake real‐world decision context.We used the Moral Foundations Dictionary, an established list of moral words in the English language, to extract moral words from the text using Natural Language Processing. With a latent class analysis, we explored if there were qualitatively different underlying patterns in the PC patient population. We used count models to analyze different types of morality in the conversations and explore if heterogeneous patterns of morality terms in patients.We are using data from the PCCRI which was designed to understand the relation between clinical communication and patient‐centered outcomes. The 6‐month cohort data include directly observed and audio‐recorded palliative care consultations (up to first 3 visits); patient/proxy and clinician self‐report questionnaires (such as patient perceptions and preferences; clinician perceptions) both before and the day after consultation; post‐consultation in‐depth interviews; and medical/administrative records (such as disease status; treatment utilization).We found two latent classes: class one in which patients did not use many expressions of morality (about two thirds) in their PC consultations and class two in which patients did (one third). Age, race, education, spiritual needs, and whether a patient was affiliated with Christianity or another religion were all associated with class membership. Gender, financial hardship, and preference for longevity‐focused over comfort‐focused treatment near EOL did not affect class membership. In our count models, we also found that some patient characteristics were associated with the use of different moral terminology.This study is among the first to use text data from a real‐world situation to extract information regarding individual moral expressions and the relation with patient characteristics, attitudes, and emotions.The results of this study are relevant to those who seek to improve the quality of communication in order to achieve better values‐concordant treatment at EOL. From text data, this study identifies moral values among patients in a palliative care setting. The effect of moral values on EOL care preference resulting in PC treatment utilization illustrates the contribution to empirical investigation of human motivations related to morality. It highlights the importance of differentiating prognosis communication with respect to patients’ different moral values influencing their EOL preferences. This study also emphasizes the importance of using text data for health services research in addition to existing quantitative and qualitative HSR methods.

Challenges to Using Big Data in Health Services Research

Given the shift in current healthcare trends toward digitization of storing information, there has been an increase in the number of studies using administrative databases. These databases provide a powerful tool to conduct research on outcomes, health services, and epidemiology. However, these databases have limitations and biases that should be considered. Given the sensitive information regarding patients’ health in the database, security clearances must be granted before data is accessed. Furthermore, algorithms to link the different variables to create a cohort of people with specific disease are imperfect and may not yield an accurate representation. Due to a large volume of records, a statistically significant finding may be observed, but may provide insignificant clinical results. Despite the current limitations, administrative databases provide powerful data that researchers can use to identify gaps in performance to improve the healthcare system.

Processing of Electronic Medical Records for Health Services Research in an Academic Medical Center: Methods and Validation.

BackgroundElectronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity, and lack of suitable measures in important domains still hinder the progress.ObjectiveThe objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas.MethodsOn the basis of a local hospital EMR system in Singapore, we developed a structured framework for EMR data processing, including standardization and phenotyping of diagnosis codes, construction of cohort with multilevel views, and generation of variables and proxy measures to supplement primary data. Disease complexity was estimated by Charlson Comorbidity Index (CCI) and Polypharmacy Score (PPS), whereas socioeconomic status (SES) was estimated by housing type. Validity of modified diagnosis codes and derived measures were investigated.ResultsVisit-level (N=7,778,761) and patient-level records (n=549,109) were generated. The International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes were standardized to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) with a mapping rate of 87.1%. In all, 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software by Agency for Healthcare Research and Quality. Diagnosis codes that underwent modification (truncation or zero addition) in standardization and phenotyping procedures had the modification validated by physicians, with validity rates of more than 90%. Disease complexity measures (CCI and PPS) and SES were found to be valid and robust after a correlation analysis and a multivariate regression analysis. CCI and PPS were correlated with each other and positively correlated with health care utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with higher SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity, and health care utilization in those aged above 65 years and those aged 65 years or younger.ConclusionsThe framework proposed in this study would be useful for other researchers working with EMR data for health services research. Further analyses would be needed to better understand differences observed in the cohorts.

Prevalence and treatment of hip and knee osteoarthritis in people aged 60 years or older in Germany: an analysis based on health insurance claims data.

ObjectiveOsteoarthritis (OA) is highly prevalent throughout the world, especially in the elderly population, and is strongly associated with patients’ frailty. However, little is known about the prevalence and treatment of OA in elderly patients in routine clinical care in Germany.Materials and methodsAs a part of Linking Patient-Reported Outcomes with CLAIms Data for Health Services Research in Rheumatology (PROCLAIR), a cross-sectional study using claims data from a large Germany statutory health insurance (BARMER) was conducted. We included people aged 60 years or older and assessed the prevalence of OA of the hip or knee, defined as having outpatient diagnoses (ICD: M16 or M17) in at least two quarters of 2014. The use of conservative treatment, including analgesics and physical therapy, and total joint replacement was studied. Analyses were stratified by age, sex, comorbidities, and level of care dependency defined by social law.ResultsA total of 595,754 patients (mean age: 74.9 years; 69.8% female) were diagnosed with OA (21.8%), with the highest prevalence in those between 80 and 89 years (31.0%) and in females compared to males (23.9% vs 18.3%). Prevalence decreased with increasing level of care dependency from 30.5% in patients with a low level (0/1) to 18.7% in the highest level of care dependency. A total of 63.4% of the patients with OA received analgesics, with higher use with increasing age. Physical therapy was prescribed to 43.1% of the patients, but use decreased with age. In all, 5.3% of the patients received total joint replacement in 2014.ConclusionThe lower frequency of coded OA with increasing level of care dependency may reflect underdiagnosis, and patients with many other medical problems seem to be at risk for inadequate recognition and treatment of their OA.

Analysing complex linked administrative data in health services research: Issues and solutions

IntroductionLinked administrative data are increasingly being used to evaluate the impact of health policy on health-service use/cost because they can comprehensively capture whole of population interactions with the health system. These analyses are complex comprising unbalanced panels and are at risk of endogeneity and associated problems.
 Objectives and ApproachWe evaluated the impact of changes in regularity of general practitioner contact on diabetes related hospitalisation before and after care coordination policies using whole of population, person-level linked primary care, hospital, Electoral Roll and death records. Complex panel random-effects modelling techniques were required due to the unbalanced structure of the data (individuals could exit and re-enter the study repeatedly), over-dispersion and high proportion of zeros, changes in availability of tests (ascertainment bias), the likelihood of prior health service use influencing the dependent variable (initial conditions and simultaneity/reverse causality bias) and likely correlation of observed and unobserved variables.
 ResultsMultivariable zero-inflated negative binomial and Cragg-hurdle clustered robust regression, which include separate components to model zero and non-zero outcomes, were required for these data. Mundlak variables (group-means of time-varying variables) were used to relax the assumption in the random-effects estimator that the observed variables were uncorrelated with the unobserved ones. Prior health service use was adjusted for using 4-year lags of GP contact and one-year lag of hospitalisation. The initial value of the dependent variable resolved the “initial condition” problem. Ascertainment bias was addressed using the number of years available for identification for each person as a covariate. AIC/BIC values were used to identify the best model. We found that more regular GP contact was associated with fewer hospitalisations, however this attenuated over time.
 Conclusion/ImplicationsAvailability of linked data, together with increases in computing power, has vastly increased its potential for use. This has also increased the complexity of analyses being undertaken necessitating recognizing and addressing problems, such as endogeneity, that arise due to the observational nature of the studies undertaken.

Professional identity, career choices, and working conditions of future and young dentists in Germany \u2013 study design and methods of a nationwide comprehensive survey

BackgroundLittle is known regarding young and future dentists’ career choices, professional identity, and working conditions in Germany. While the dental healthcare environment and demands in treatment are changing, it remains unclear what job perceptions young dentists have developed at the beginning of their work life and if and how these perceptions change during the subsequent years. The aim of this study was to survey future and young dentists regarding their professional identity, planned career paths, and working conditions and strains to understand career decisions and choices and enable policy makers to include future dentists‘ views and expectations in their decisions.Methods/designThis study is a longitudinal nationwide survey over a time span of 4 years of dental students and young dentists in Germany and is comprised of three waves. The first wave focuses on dental students in their final year before the state examination and is composed of a qualitative pre-study in the form of focus groups and a quantitative main survey in the form of a questionnaire. The end points were established to analyse (1) the professional identity of the young future dentists; (2) their career paths, preparation for a career, and basic career conditions; and (3) perceived conditions and strains. The aim of the overall survey was to depict the development of these three aspects during the first years of work life. All of the questions were evaluated with a descriptive univariate analysis. The analysed subgroups were grouped according to gender, target working condition (employed/self-employed), and primary socialisation (parents dentists/parents not dentists).DiscussionTo our knowledge, this is the only study which focuses on career choices, professional identity, and working conditions of future and young dentists in Germany. The longitudinal observation provides information that is essential for professional and purposive dental health care planning, and to meet the oral health demands and needs of the German population appropriately over the long term.Trial registrationGerman Health Services Research Data Bank VfD_Y-Dent_14_003759.

Development of a Natural Language Processing Engine to Generate Bladder Cancer Pathology Data for Health Services Research

To take the first step toward assembling population-based cohorts of patients with bladder cancer with longitudinal pathology data, we developed and validated a natural language processing (NLP) engine that abstracts pathology data from full-text pathology reports. Using 600 bladder pathology reports randomly selected from the Department of Veterans Affairs, we developed and validated an NLP engine to abstract data on histology, invasion (presence vs absence and depth), grade, the presence of muscularis propria, and the presence of carcinoma in situ. Our gold standard was based on an independent review of reports by 2 urologists, followed by adjudication. We assessed the NLP performance by calculating the accuracy, the positive predictive value, and the sensitivity. We subsequently applied the NLP engine to pathology reports from 10,725 patients with bladder cancer. When comparing the NLP output to the gold standard, NLP achieved the highest accuracy (0.98) for the presence vs the absence of carcinoma in situ. Accuracy for histology, invasion (presence vs absence), grade, and the presence of muscularis propria ranged from 0.83 to 0.96. The most challenging variable was depth of invasion (accuracy 0.68), with an acceptable positive predictive value for lamina propria (0.82) and for muscularis propria (0.87) invasion. The validated engine was capable of abstracting pathologic characteristics for 99% of the patients with bladder cancer. NLP had high accuracy for 5 of 6 variables and abstracted data for the vast majority of the patients. This now allows for the assembly of population-based cohorts with longitudinal pathology data.

Pain, depressive symptoms and medication in German patients with rheumatoid arthritis-results from the linking patient-reported outcomes with claims data for health services research in rheumatology (PROCLAIR) study.

Pain and depressive symptoms are common in patients with rheumatoid arthritis (RA). Information on the prevalence and treatment of both conditions in German RA patients is scarce. Using data from a nationwide statutory health insurance fund (BARMER GEK), 6193 RA patients aged 18 to 79years were provided with a questionnaire covering a variety of items such as demographics, medical condition and quality of life in 2015. Pain caused by the joint disorder (11-point scale) was classified as none existent/mild, moderate or severe. Depressive symptoms were determined using the World Health Organization's five-item Well-being Index and categorized as none existent, mild or moderate/severe. Another item covered additional use of over-the-counter drugs. Data were linked to dispensation records. A total of 3140 RA patients were included. Median age was 66years (79% female). About 70% of patients were classified as having moderate or severe pain. Depressive symptoms were found in 52% and were far more common among patients with higher pain levels. Analgesic treatment ranged from 45% to 76% (non-opioid analgesics) and from 6% to 33% (opioids) in patients with no/mild pain and those reporting severe pain, respectively. In patients reporting moderate or severe pain, substantially higher prevalences of opioid use were observed among those with depressive symptoms. Depending on depressive symptoms, antidepressant use ranged from 7% to 37%. Overall, over-the-counter drug use varied between 30% and 59%. Pain and depressive symptoms are highly prevalent in German RA patients, often present together and influence each other's treatment. Copyright © 2017 John Wiley & Sons, Ltd.

Lay involvement in the analysis of qualitative data in health services research: a descriptive study

Plain English summaryPlain English summaryThere is a consensus that patients and the public should be involved in research in a meaningful way. However, to date, lay people have been mostly involved in developing research ideas and commenting on patient information.We previously published a paper describing our experience with lay partners conducting observations in a study of how patients in hospital are involved with their medicines. In a later part of the same study, lay partners were also involved in analysing interviews that a researcher had conducted with patients, carers and healthcare professionals about patient and carer involvement with medicines in hospital. We therefore wanted to build on our previous paper and report on our experiences with lay partners helping to conduct data analysis. We therefore interviewed the lay members and researchers involved in the analysis to find out their views.Both lay members and researchers reported that lay partners added value to the study by bringing their own perspectives and identifying further areas for the researcher to look for in the interviews. In this way researchers and lay partners were able to work together to produce a richer analysis than would have been possible from either alone.Background It is recognised that involving lay people in research in a meaningful rather than tokenistic way is both important and challenging. In this paper, we contribute to this debate by describing our experiences of lay involvement in data analysis.Methods We conducted semi-structured interviews with the lay partners and researchers involved in qualitative data analysis in a wider study of inpatient involvement in medication safety. The interviews were transcribed verbatim and coded using open thematic analysis.Results We interviewed three lay partners and the three researchers involved. These interviews demonstrated that the lay members added value to the analysis by bringing their own perspectives; these were systematically integrated into the analysis by the lead researcher to create a synergistic output. Some challenges arose, including difficulties in recruiting a diverse range of members of the public to carry out the role; however there were generally fewer challenges in data analysis than there had been with our previous experience of lay partners’ involvement in data collection.Conclusions Lay members can add value to health services research by being involved in qualitative data analysis.

Validation of SHI Claims Data Exemplified by Gender-specific Diagnoses

Aim: Use of statutory health insurance (SHI) data in health services research is increasing steadily and questions of validity are gaining importance. Using gender-specific diagnosis as an example, the aim of this study was to estimate the prevalence of implausible diagnosis and demonstrate an internal validation strategy. Method: The analysis is based on the SHI data from Baden-Württemberg for 2012. Subject of validation are gender-specific outpatient diagnoses that mismatch with the gender of the insured. To uncover this implausibility, it is necessary to clarify whether the diagnosis or the gender is wrong. The validation criteria used were the presence of further gender-specific diagnoses, the presence of gender-specific settlement items, the specialization of the physician in charge and the gender assignment of the first name of the insured. To review the quality of the validation, it was verified if the gender was changed during the following year. Results: Around 5.1% of all diagnoses were gender-specific and there was a mismatch between diagnosis and gender in 0.04% of these cases. All validation criteria were useful to sort out implausibility, whereas the last one was the most effective. Only 14% remained unsolved. From the total of 1 145 insured with implausible gender-specific diagnoses, one year later 128 had a new gender (in the data). 119 of these cases were rightly classified as insured with wrong gender and 9 cases were in the unsolved group. This confirms that the validation works well. Conclusion: Implausibility in SHI data is relatively small and can be solved with appropriate validation criteria. When validating SHI data, it is advisable to question all data used critically, to use multiple validation criteria instead of just one and to abandon the idea that reality and the associated data conform to standardized norms. Keeping these aspects in mind, analysis of SHI data is a good starting point for research in health services.

Setting up a regional health system database for seamless population health management in Singapore

Objective: Understanding the health and health service utilization of the population is critical for Regional Health System’s (RHS) population health management (PHM) initiatives in Singapore. The RHS database is a collaborative effort toward developing a national architecture for healthcare utilization data across diverse clinical systems with disparate data models. This manuscript describes the setup of an RHS database which would facilitate big data analytics for proactive population health management and health services research. Materials and methods: The RHS database is a conglomeration of four isolated databases from the three RHSs. It contains linked National Healthcare Group (NHG) polyclinic visit records, specialist outpatient clinic visit records, hospital discharge records from Tan Tock Seng Hospital (TTSH), National University Hospital (NUH) and Alexandra Hospital (AH), chronic disease management system (CDMS) records and mortality records from local registries. The data linkage process was conducted using the unique identification number (NRIC) as the linking variable. The final anonymized database has multiple interconnected tables that includes patient demographics, chronic disease and healthcare utilization information. Results: Over 2.8 million patients had contact with the three RHSs from 2008 to 2013. The database facilitated risk stratification of patients based on their past healthcare utilization and chronic disease information. This database aids in understanding the cross-utilization of healthcare services across the three RHSs and can help address the challenges of setting up a distinct geographical boundary for individual RHSs. Conclusions: The RHS database has been established with the intention to support the secondary use of administrative health data in health services research and proactive PHM in Singapore.

The care.data consensus? A qualitative analysis of opinions expressed on Twitter.

BackgroundLarge, integrated datasets can be used to improve the identification and management of health conditions. However, big data initiatives are controversial because of risks to privacy. In 2014, NHS England launched a public awareness campaign about the care.data project, whereby data from patients’ medical records would be regularly uploaded to a central database. Details of the project sparked intense debate across a number of platforms, including social media sites such as Twitter. Twitter is increasingly being used to educate and inform patients and care providers, and as a source of data for health services research. The aim of the study was to identify and describe the range of opinions expressed about care.data on Twitter for the period during which a delay to this project was announced, and provide insight into the strengths and flaws of the project.MethodsTweets with the hashtag #caredata were collected using the NCapture tool for NVivo. Methods of qualitative data analysis were used to identify emerging themes. Tweets were coded and analysed in-depth within and across themes.ResultsThe dataset consisted of 9895 tweets, captured over 18 days during February and March 2014. Retweets (6118, 62 %) and spam (240, 2 %) were excluded. The remaining 3537 tweets were posted by 904 contributors, and coded into one or more of 50 sub-themes, which were organised into 9 key themes. These were: informed consent and the default ‘opt-in’, trust, privacy and data security, involvement of private companies, legal issues and GPs’ concerns, communication failure and confusion about care.data, delayed implementation, patient-centeredness, and potential of care.data and the ideal model of implementation.ConclusionsVarious concerns were raised about care.data that appeared to be shared by those both for and against the project. Qualitatively analysing tweets enabled us to identify a range of concerns about care.data and how these might be overcome, for example, by increasing the involvement of stakeholders and those with expert knowledge. Our findings also highlight the risks of not considering public opinion, such as the potential for patient safety failures resulting from a lack of trust in the healthcare system. However, caution is advised if using Twitter as a stand-alone data source, as contributors may lie more heavily on one side of a debate than another. A mixed-methods approach would have enabled us to complement this data with a more representative overview.

The Fifth German Oral Health Study (Fünfte Deutsche Mundgesundheitsstudie, DMS V) - rationale, design, and methods.

BackgroundOral diseases rank among the most prevalent non-communicable diseases in modern societies. In Germany, oral epidemiological data show that both dental caries and periodontal diseases are highly prevalent, though significant improvements in oral health has been taking in the population within the last decades, particularly in children. It is, therefore, the aim of the Fifth German Oral Health Study (DMS V) to actualize the data on current oral health status and to gather information on oral health behavior and risk factors. In addition to current oral health monitoring, the study will also permit conclusions about trends in the development of oral health in Germany between 1989 and 2014.Methods/DesignDMS V is a cross-sectional, multi-center, nationwide representative, socio-epidemiological study to investigate the oral health status und behavior of the German resident population in four age cohorts. Study participants are children (12-year-olds), adults (35- to 44-year-olds), young olds (65- to 74-year-olds), and old olds (75- to 100-year-olds) who are drawn from local residents’ registration offices. Social-science investigation parameters concern subjective perceptions and attitudes regarding oral health and nutrition, sense of coherence, and socio-demographic data. Clinical oral parameters are tooth loss, caries and periodontitis, prosthodontic status, further developmental and acquired dental hard tissue and mucosal lesions. To ensure reproducibility, the dental investigators are trained and calibrated by experts and multiple reliability checks are performed throughout the field phase. Statistical analyses are calculated according to a detailed statistical analysis plan.DiscussionThe DMS studies first performed in 1989, 1992 and repeated in 1997 and 2005 are the only cross-sectional oral health studies conducted in Germany on a population-based national representative level. Updated prevalence and trend analyses of key oral diseases are, therefore, of major epidemiological and health services research interest.Trial registrationGerman Health Services Research Data Bank VfD_DMSV_13_002152

Registry data in health services research: reflection of reality or prohibitive uncertainty?

Journal of Surgical OncologyVolume 110, Issue 3 p. 223-224 Commentary Registry data in health services research: Reflection of reality or prohibitive uncertainty? Daniel E. Abbott MD, Corresponding Author Daniel E. Abbott MD Division of Surgical Oncology, Department of Surgery, University of Cincinnati Medical Center, Cincinnati, Ohio Correspondence to: Daniel E. Abbott, MD, Assistant Professor of Surgery, Division of Surgical Oncology, Department of Surgery, University of Cincinnati Medical Center, 234 Goodman Street, ML 0772, Cincinnati, OH 45219. Fax: +1-513-584-0459. E-mail: [email protected]Search for more papers by this author Daniel E. Abbott MD, Corresponding Author Daniel E. Abbott MD Division of Surgical Oncology, Department of Surgery, University of Cincinnati Medical Center, Cincinnati, Ohio Correspondence to: Daniel E. Abbott, MD, Assistant Professor of Surgery, Division of Surgical Oncology, Department of Surgery, University of Cincinnati Medical Center, 234 Goodman Street, ML 0772, Cincinnati, OH 45219. Fax: +1-513-584-0459. E-mail: [email protected]Search for more papers by this author First published: 21 May 2014 https://doi.org/10.1002/jso.23630Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume110, Issue3September 1, 2014Pages 223-224 RelatedInformation

H.4. Workshop: Using Health Claims Data in Health Services Research – a Blessing or a Curse?<subtitle>Organised by: Department of Quality of Care and Health Economics within the Centre of Nutrition, Prevention and Health Services, The Netherlands</subtitle>

H.4. Workshop: Using Health Claims Data in Health Services Research – a Blessing or a Curse?<subtitle>Organised by: Department of Quality of Care and Health Economics within the Centre of Nutrition, Prevention and Health Services, The Netherlands</subtitle>