Preliminary Safety Analysis of a Wearable Clinic for the Early Detection of Psychotic Relapse.

We discuss the preliminary safety analysis of a smartphone-based intervention for early detection of psychotic relapse. We briefly describe how we identified patient safety hazards associated with the system and how measures were defined to mitigate these hazards.

Behavioural Phenotyping of Daily Activities Relevant to Social Functioning Based on Smartphone-Collected Geolocation Data.

Smartphones offer new opportunities to monitor health-related behaviours in the real world. This allows researchers to go beyond traditional data collection methods, such as interviews and questionnaires that suffer from recall bias and low spatio-temporal resolution. In this study, we present an experiment that uses advanced analytical methods to identify daily activities relevant to assess social functioning, from geolocation data. Twenty-one healthy volunteers used a smartphone to continuously record their GPS location for up to 10 days. Participants also completed a diary to record their daily activities that was used as ground truth. Using clustering algorithms and semantic enrichment methods we were able to predict these activities from the GPS data with a precision of 0.75 (standard deviation [SD] 0.13) and a recall of 0.60 (SD 0.11). Although performed on a limited sample, our study shows potential for continuous, and passive geolocation-based monitoring of patient behaviour in mental health.

Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review.

OBJECTIVE: The study sought to explore to what extent geolocation data has been used to study serious mental illness (SMI). SMIs such as bipolar disorder and schizophrenia are characterized by fluctuating symptoms and sudden relapse. Currently, monitoring of people with an SMI is largely done through face-to-face visits. Smartphone-based geolocation sensors create opportunities for continuous monitoring and early intervention. MATERIALS AND METHODS: We searched MEDLINE, PsycINFO, and Scopus by combining terms related to geolocation and smartphones with SMI concepts. Study selection and data extraction were done in duplicate. RESULTS: Eighteen publications describing 16 studies were included in our review. Eleven studies focused on bipolar disorder. Common geolocation-derived digital biomarkers were number of locations visited (n = 8), distance traveled (n = 8), time spent at prespecified locations (n = 7), and number of changes in GSM (Global System for Mobile communications) cell (n = 4). Twelve of 14 publications evaluating clinical aspects found an association between geolocation-derived digital biomarker and SMI concepts, especially mood. Geolocation-derived digital biomarkers were more strongly associated with SMI concepts than other information (eg, accelerometer data, smartphone activity, self-reported symptoms). However, small sample sizes and short follow-up warrant cautious interpretation of these findings: of all included studies, 7 had a sample of fewer than 10 patients and 11 had a duration shorter than 12 weeks. CONCLUSIONS: The growing body of evidence for the association between SMI concepts and geolocation-derived digital biomarkers shows potential for this instrument to be used for continuous monitoring of patients in their everyday lives, but there is a need for larger studies with longer follow-up times.

Acute kidney injury in the UK: a replication cohort study of the variation across three regional populations.

OBJECTIVES: A rapid growth in the reported rates of acute kidney injury (AKI) has led to calls for greater attention and greater resources for improving care. However, the reported incidence of AKI also varies more than tenfold between previous studies. Some of this variation is likely to stem from methodological heterogeneity. This study explores the extent of cross-population variation in AKI incidence after minimising heterogeneity. DESIGN: Population-based cohort study analysing data from electronic health records from three regions in the UK through shared analysis code and harmonised methodology. SETTING: Three populations from Scotland, Wales and England covering three time periods: Grampian 2003, 2007 and 2012; Swansea 2007; and Salford 2012. PARTICIPANTS: All residents in each region, aged 15 years or older. MAIN OUTCOME MEASURES: Population incidence of AKI and AKI phenotype (severity, recovery, recurrence). Determined using shared biochemistry-based AKI episode code and standardised by age and sex. RESULTS: Respectively, crude AKI rates (per 10 000/year) were 131, 138, 139, 151 and 124 (p=0.095), and after standardisation for age and sex: 147, 151, 146, 146 and 142 (p=0.257) for Grampian 2003, 2007 and 2012; Swansea 2007; and Salford 2012. The pattern of variation in crude rates was robust to any modifications of the AKI definition. Across all populations and time periods, AKI rates increased substantially with age from ~20 to ~550 per 10 000/year among those aged <40 and ≥70 years. CONCLUSION: When harmonised methods are used and age and sex differences are accounted for, a similar high burden of AKI is consistently observed across different populations and time periods (~150 per 10 000/year). There are particularly high rates of AKI among older people. Policy-makers should be careful not draw simplistic assumptions about variation in AKI rates based on comparisons that are not rigorous in methodological terms.

The influence of patient portals on users' decision making is insufficiently investigated: A systematic methodological review.

BACKGROUND: Patient portals are considered valuable conduits for supporting patients' self-management. However, it is unknown why they often fail to impact on health care processes and outcomes. This may be due to a scarcity of robust studies focusing on the steps that are required to induce improvement: users need to effectively interact with the portal (step 1) in order to receive information (step 2), which might influence their decision-making (step 3). We aimed to explore this potential knowledge gap by investigating to what extent each step has been investigated for patient portals, and explore the methodological approaches used. METHODS: We performed a systematic literature review using Coiera's information value chain as a guiding theoretical framework. We searched MEDLINE and Scopus by combining terms related to patient portals and evaluation methodologies. Two reviewers selected relevant papers through duplicate screening, and one extracted data from the included papers. RESULTS: We included 115 articles. The large majority (n = 104) evaluated aspects related to interaction with patient portals (step 1). Usage was most often assessed (n = 61), mainly by analysing system interaction data (n = 50), with most authors considering participants as active users if they logged in at least once. Overall usability (n = 57) was commonly assessed through non-validated questionnaires (n = 44). Step 2 (information received) was investigated in 58 studies, primarily by analysing interaction data to evaluate usage of specific system functionalities (n = 34). Eleven studies explicitly assessed the influence of patient portals on patients' and clinicians' decisions (step 3). CONCLUSIONS: Whereas interaction with patient portals has been extensively studied, their influence on users' decision-making remains under-investigated. Methodological approaches to evaluating usage and usability of portals showed room for improvement. To unlock the potential of patient portals, more (robust) research should focus on better understanding the complex process of how portals lead to improved health and care.

Presentation of laboratory test results in patient portals: influence of interface design on risk interpretation and visual search behaviour.

BACKGROUND: Patient portals are considered valuable instruments for self-management of long term conditions, however, there are concerns over how patients might interpret and act on the clinical information they access. We hypothesized that visual cues improve patients' abilities to correctly interpret laboratory test results presented through patient portals. We also assessed, by applying eye-tracking methods, the relationship between risk interpretation and visual search behaviour. METHODS: We conducted a controlled study with 20 kidney transplant patients. Participants viewed three different graphical presentations in each of low, medium, and high risk clinical scenarios composed of results for 28 laboratory tests. After viewing each clinical scenario, patients were asked how they would have acted in real life if the results were their own, as a proxy of their risk interpretation. They could choose between: 1) Calling their doctor immediately (high interpreted risk); 2) Trying to arrange an appointment within the next 4 weeks (medium interpreted risk); 3) Waiting for the next appointment in 3 months (low interpreted risk). For each presentation, we assessed accuracy of patients' risk interpretation, and employed eye tracking to assess and compare visual search behaviour. RESULTS: Misinterpretation of risk was common, with 65% of participants underestimating the need for action across all presentations at least once. Participants found it particularly difficult to interpret medium risk clinical scenarios. Participants who consistently understood when action was needed showed a higher visual search efficiency, suggesting a better strategy to cope with information overload that helped them to focus on the laboratory tests most relevant to their condition. CONCLUSIONS: This study confirms patients' difficulties in interpreting laboratories test results, with many patients underestimating the need for action, even when abnormal values were highlighted or grouped together. Our findings raise patient safety concerns and may limit the potential of patient portals to actively involve patients in their own healthcare.

Informatics for Health 2017: Advancing both science and practice.

INTRODUCTION: The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress. DISCUSSION: The twin programmes of "Big Data" and "Digital Health" are not always joined up by coherent policy and investment priorities. Substantial global investment in health IT and data science has led to sound progress but highly variable outcomes. Society needs an approach that brings together the science and the practice of health informatics. The goal is multi-level Learning Health Systems that consume and intelligently act upon both patient data and organizational intervention outcomes. CONCLUSION: Informatics for Health demonstrated the art of the possible, seen in the breadth and depth of our contributions. We call upon policy makers, research funders and programme leaders to learn from this joined-up approach.

Patient Portal Adoption Rates: A Systematic Literature Review and Meta-Analysis.

Despite the increasing availability of online patient portals that provide access to electronic health records, little is known about their adoption by patients. We systematically reviewed the literature to investigate adoption of patient portals across studies. We searched MEDLINE and Scopus to identify relevant papers. We included 40 studies: 24 were controlled experiments, with prospective data collection in an actively recruited population; 16 were real-world experiments, with adoption being evaluated retrospectively after system deployment in clinical practice. Our meta-analysis showed an overall mean adoption rate of 52% (95% Confidence Interval [CI], 42 to 62%). Rates differed markedly between study types: controlled experiments yielded a mean adoption rate of 71% (95% CI 64 to 79%), compared to 23% (95% CI, 13 to 33%) in real-world experiments. This difference was confirmed in a meta-regression analysis of the influence of study characteristics on adoption rates. Our findings suggest that adoption rates reported in controlled studies do not reflect those in everyday clinical practice. Until we understand how to effectively increase adoption, patient portals are unlikely to consistently lead to improvements in care processes and health outcomes.

Predicting mortality from change-over-time in the Charlson Comorbidity Index: A retrospective cohort study in a data-intensive UK health system.

Multimorbidity is common among older people and presents a major challenge to health systems worldwide. Metrics of multimorbidity are, however, crude: focusing on measuring comorbid conditions at single time-points rather than reflecting the longitudinal and additive nature of chronic conditions. In this paper, we explore longitudinal comorbidity metrics and their value in predicting mortality.Using linked primary and secondary care data, we conducted a retrospective cohort study on adults in Salford, UK from 2005 to 2014 (n = 287,459). We measured multimorbidity with the Charlson Comorbidity Index (CCI) and quantified its changes in various time windows. We used survival models to assess the relationship between CCI changes and mortality, controlling for gender, age, baseline CCI, and time-dependent CCI. Goodness-of-fit was assessed with the Akaike Information Criterion and discrimination with the c-statistic.Overall, 15.9% patients experienced a change in CCI after 10 years, with a mortality rate of 19.8%. The model that included gender and time-dependent age, CCI, and CCI change across consecutive time windows had the best fit to the data but equivalent discrimination to the other time-dependent models. The absolute CCI score gave a constant hazard ratio (HR) of around 1.3 per unit increase, while CCI change afforded greater prognostic impact, particularly when it occurred in shorter time windows (maximum HR value for the 3-month time window, with 1.63 and 95% confidence interval 1.59-1.66).Change over time in comorbidity is an important but overlooked predictor of mortality, which should be considered in research and care quality management.

An external validation of models to predict the onset of chronic kidney disease using population-based electronic health records from Salford, UK.

BACKGROUND: Chronic kidney disease (CKD) is a major and increasing constituent of disease burdens worldwide. Early identification of patients at increased risk of developing CKD can guide interventions to slow disease progression, initiate timely referral to appropriate kidney care services, and support targeting of care resources. Risk prediction models can extend laboratory-based CKD screening to earlier stages of disease; however, to date, only a few of them have been externally validated or directly compared outside development populations. Our objective was to validate published CKD prediction models applicable in primary care. METHODS: We synthesised two recent systematic reviews of CKD risk prediction models and externally validated selected models for a 5-year horizon of disease onset. We used linked, anonymised, structured (coded) primary and secondary care data from patients resident in Salford (population ~234 k), UK. All adult patients with at least one record in 2009 were followed-up until the end of 2014, death, or CKD onset (n = 178,399). CKD onset was defined as repeated impaired eGFR measures over a period of at least 3 months, or physician diagnosis of CKD Stage 3-5. For each model, we assessed discrimination, calibration, and decision curve analysis. RESULTS: Seven relevant CKD risk prediction models were identified. Five models also had an associated simplified scoring system. All models discriminated well between patients developing CKD or not, with c-statistics around 0.90. Most of the models were poorly calibrated to our population, substantially over-predicting risk. The two models that did not require recalibration were also the ones that had the best performance in the decision curve analysis. CONCLUSIONS: Included CKD prediction models showed good discriminative ability but over-predicted the actual 5-year CKD risk in English primary care patients. QKidney, the only UK-developed model, outperformed the others. Clinical prediction models should be (re)calibrated for their intended uses.

I-Maculaweb: A Tool to Support Data Reuse in Ophthalmology.

This paper intends to present a Web-based application to collect and manage clinical data and clinical trials together in a unique tool. I-maculaweb is a user-friendly Web-application designed to manage, share, and analyze clinical data from patients affected by degenerative and vascular diseases of the macula. The unique and innovative scientific and technological elements of this project are the integration with individual and population data, relevant for degenerative and vascular diseases of the macula. Clinical records can also be extracted for statistical purposes and used for clinical decision support systems. I-maculaweb is based on an existing multilevel and multiscale data management model, which includes general principles that are suitable for several different clinical domains. The database structure has been specifically built to respect laterality, a key aspect in ophthalmology. Users can add and manage patient records, follow-up visits, treatment, diagnoses, and clinical history. There are two different modalities to extract records: one for the patient's own center, in which personal details are shown and the other for statistical purposes, where all center's anonymized data are visible. The Web-platform allows effective management, sharing, and reuse of information within primary care and clinical research. Clear and precise clinical data will improve understanding of real-life management of degenerative and vascular diseases of the macula as well as increasing precise epidemiologic and statistical data. Furthermore, this Web-based application can be easily employed as an electronic clinical research file in clinical studies.

Development and preliminary validation of a dynamic, patient-tailored method to detect abnormal laboratory test results.

Laboratory test results in primary care are flagged as 'abnormal' when they fall outside a population-based Reference Interval (RI), typically generating many alerts with a low specificity. In order to decrease alert frequency while retaining clinical relevance, we developed a method to assess dynamic, patient-tailored RIs based on mixed-effects linear regression models. Potassium test results from primary care were used as proof-of-concept test bed. Clinical relevance was assessed via a survey administered to general practitioners (GPs). Overall, the dynamic, patient-tailored method and the combination of both methods flagged 20% and 36% fewer values as abnormal than the population-based method. Nineteen out of 43 invited GPs (44%) completed the survey. The population-based method yielded a better sensitivity than the patient-tailored and the combined methods (0.51 vs 0.41 and 0.38, respectively) but a lower PPV (0.66 vs 0.67 and 0.76, respectively). We conclude that a combination of population-based and patient-tailored RIs can improve the detection of abnormal laboratory results. We suggest that lab values outside both RIs be flagged with high priority in clinical practice.

Adoption of clinical decision support in multimorbidity: a systematic review.

BACKGROUND: Patients with multiple conditions have complex needs and are increasing in number as populations age. This multimorbidity is one of the greatest challenges facing health care. Having more than 1 condition generates (1) interactions between pathologies, (2) duplication of tests, (3) difficulties in adhering to often conflicting clinical practice guidelines, (4) obstacles in the continuity of care, (5) confusing self-management information, and (6) medication errors. In this context, clinical decision support (CDS) systems need to be able to handle realistic complexity and minimize iatrogenic risks. OBJECTIVE: The aim of this review was to identify to what extent CDS is adopted in multimorbidity. METHODS: This review followed PRISMA guidance and adopted a multidisciplinary approach. Scopus and PubMed searches were performed by combining terms from 3 different thesauri containing synonyms for (1) multimorbidity and comorbidity, (2) polypharmacy, and (3) CDS. The relevant articles were identified by examining the titles and abstracts. The full text of selected/relevant articles was analyzed in-depth. For articles appropriate for this review, data were collected on clinical tasks, diseases, decision maker, methods, data input context, user interface considerations, and evaluation of effectiveness. RESULTS: A total of 50 articles were selected for the full in-depth analysis and 20 studies were included in the final review. Medication (n=10) and clinical guidance (n=8) were the predominant clinical tasks. Four studies focused on merging concurrent clinical practice guidelines. A total of 17 articles reported their CDS systems were knowledge-based. Most articles reviewed considered patients' clinical records (n=19), clinical practice guidelines (n=12), and clinicians' knowledge (n=10) as contextual input data. The most frequent diseases mentioned were cardiovascular (n=9) and diabetes mellitus (n=5). In all, 12 articles mentioned generalist doctor(s) as the decision maker(s). For articles reviewed, there were no studies referring to the active involvement of the patient in the decision-making process or to patient self-management. None of the articles reviewed adopted mobile technologies. There were no rigorous evaluations of usability or effectiveness of the CDS systems reported. CONCLUSIONS: This review shows that multimorbidity is underinvestigated in the informatics of supporting clinical decisions. CDS interventions that systematize clinical practice guidelines without considering the interactions of different conditions and care processes may lead to unhelpful or harmful clinical actions. To improve patient safety in multimorbidity, there is a need for more evidence about how both conditions and care processes interact. The data needed to build this evidence base exist in many electronic health record systems and are underused.

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach.

BACKGROUND: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) "black-box" approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). METHODS: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients' attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. RESULTS: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians' decision pathways to diagnose AMD. CONCLUSIONS: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support.

A comparison of inpatient admissions in 2012 from two European countries.

INTRODUCTION: This study compares the trends of HIV inpatient admissions between a London tertiary HIV centre (United Kingdom) and four infectious disease wards in Italy (IT) to recognize common patterns across Europe. METHODS: Data regarding HIV inpatient admissions was collected by using discharge diagnostic codes from 1 January to 31 December 2012, including patient demographics, combined antiretroviral therapy (cART) history, CD4, viral load (VL) and mortality rates. Discharge diagnoses were categorized according to the International Classification of Disease (ICD) 9 and 10 system. All ICD categories that reach a 3% threshold of total admissions were analyzed. RESULTS: A total of 731 admissions (257 in Italy and 474 in the United Kingdom) for 521 patients (1.5 mean admission per patient). Female admissions were higher in Italy at 22.6% (n=58) compared to 14.9% (n=47) in the United Kingdom. Median age of patients was 47 years old. There was an undetectable VL in 65.8% (n=169) of admissions in Italy and 67.1% (n=319) in the United Kingdom (p=0.385); 86.4% (n=222) and 82.4% (n=389) of admissions were on cART, respectively. Mean CD4 was 302 in Italy compared to 368 in the United Kingdom (p=0.003). Average length of admission was 16 days with a 10.2% (n=21) mortality rate in Italy compared to 8 days with 2.8% (n=9) mortality in the United Kingdom (p<0.001). HCV co-infection was present in 64.6% (n=166) in Italy and 13.5% (n=64) in the United Kingdom and commonest mode of transmission was needle use in Italy (67.3%, n=173) and men who have sex with men in the UK cohort (59.9%, n=284). The cause of inpatient admissions according to ICD codes can be seen in following Figure 1. CONCLUSIONS: Significant differences in the duration of inpatient admission and mortality rates can be observed between these two cohorts which is secondary to the impact of Hepatitis C co-infection in Italy. However increases in the number of Hepatitis C co-infection patients amongst MSM in London has been reported [1] and route of transmission in Italy is shifting towards MSM [2], therefore it is important to learn how HIV is developing and managed in a global context to help plan future for services. The UK cohort demonstrates a wider range of conditions necessitating admission, and with an ageing HIV population, this is expected to increase in the future, requiring general and specialist HIV physicians to work closely together. The HIV-RNA threshold is 400 copies/mL to account for blips according to British HIV Association (BHIVA) Guidelines 2012 [3].

Relationship between innate immunity, soluble markers and metabolic-clinical parameters in HIV+ patients ART treated with HIV-RNA<50 cp/mL.

INTRODUCTION: The persistence of immune activation and inflammation in HIV patients with HIV-RNA (VL) undetectable causes many co-morbidities [1-3]. The aim of this study is to correlate monocytes (m) and NK cell activation levels, soluble markers and oxidative stress with clinical, biochemical and metabolic data in HIV-1 infected patients with VL≤50 copies (cp)/mL on antiretroviral therapy. MATERIALS AND METHODS: Multicentre, cross-sectional study in patients with VL≤50 cp/mL and on antiretroviral therapy by at least six months. We studied: activation/homing markers (CD38, HLA-DR, CCR-2, PDL-1) on inflammatory, intermediate, proinflammatory m; activatory receptors NKp30, NKp46 and HLA-DR on NK cells; soluble inflammatory (sCD14, adiponectina, MCP-1) and stress oxidative markers (dRoms, antiRoms). Univariate analyses are performed with non-parametric and Pearson tests. The significant correlations were adjusted for possible known confounding factors (smoking, Cytomegalovirus IgG serology, Raltegravir, Protease Inhibitor [PI] therapy and HCV-RNA) with multivariate analysis. RESULTS: In the 68 patients the positive correlation between age and antiRoms was significant also after adjustment for PI use (p=0.05). The% CD8+T was associated with% proinflammatory m (p=0.043) and with their expression of CCR2 mean fluorescence intensity (MFI) (p=0.012). The% NKp46+ was positively correlated with CD4+T count (p=0.001). The fibrinogen was positively associated with dRoms (p=0.052) and the positive correlation between triglycerides and antiRoms has been confirmed (p<0.001); the impact of antiRoms on HDL/triglycerides ratio (p=0.006) was observed after adjustment for PI use. The BMI was associated with smoking (p=0.011). Only the maraviroc-treated patients showed minimal arterial pressure, fibrinogen and antiRoms lower (p=0.001, 0.004 e 0.006) and sCD14 values higher (p=0.029). CONCLUSIONS: Patients with long history of HIV infection and stable immunological and virological status showed interactions between acquired and innate immunity activation; moreover, the levels of some metabolic and inflammatory parameters correlate with oxidative stress values and innate immunity activation. The age, BMI and smoking impact metabolic and immunological parameters. The correlations between antiretroviral drugs and clinical-immunological parameters need further confirmations.

Decision time for clinical decision support systems.

Clinical decision support systems are interactive software systems designed to help clinicians with decision-making tasks, such as determining a diagnosis or recommending a treatment for a patient. Clinical decision support systems are a widely researched topic in the computer science community, but their inner workings are less well understood by, and known to, clinicians. This article provides a brief explanation of clinical decision support systems and some examples of real-world systems. It also describes some of the challenges to implementing these systems in clinical environments and posits some reasons for the limited adoption of decision-support systems in practice. It aims to engage clinicians in the development of decision support systems that can meaningfully help with their decision-making tasks and to open a discussion about the future of automated clinical decision support as a part of healthcare delivery.

Innate immunity cell activation in virologically suppressed HIV-infected maraviroc-treated patients.

This is a cross-sectional, case-control study analyzing the effect of antiretroviral therapy (ART) including or not maraviroc, on circulating monocytes and natural killer cells. Sixty-eight HIV-positive patients virologically suppressed receiving ART at least 6 months were subdivided as receiving (group 1) or not (group 2) maraviroc in their ART. Frequency of monocytes and natural killer cells, as well as their activation markers, were studied. Modulation of innate immune cells may be differently affected by combined ART.

Multicentre clinical trials' data management: a hybrid solution to exploit the strengths of electronic data capture and electronic health records systems.

BACKGROUND: Clinical Trials (CTs) are indispensable instruments for evidence-based medicine and frequently necessitate the management, sharing and analysis of large amounts of data amongst partners in different locations. METHODS: To effectively satisfy these requirements, the proposed solution combines a web platform and a Clinical Data Management System (CDMS) to exploit the strengths of Electronic Health Records (EHR) and Electronic Data Capture (EDC) systems. The core of the proposal is a relational database which has high data structuring characteristics and utilises biomedical controlled vocabularies (e.g. LOINC and ICD). In addition, units and normality ranges were collected for data comparison through the application of the Z-score transformation. RESULTS: The obtained CDMS preserves the EDC's flexibility and user autonomy and permits the creation of patient cohorts, as in the EHR. Accordingly, clinical information, after the initial recording, is available for different simultaneous multicentre CTs. Furthermore, interface runtime controls guarantee high data quality during data entering processes. Currently, the proposed system has been developed in the HIV and eye diseases fields in Italy. CONCLUSIONS: The proposed solution is flexible and suitable to perform multicentre research within a varying range of medical domains. In the future, the automatic importation of information from hospitals has been planned through an HL7 standard interface which would improve both data quantity and quality.