Search

Motivated by the ever increasing difficulties faced by laypeople in retrieving and digesting valid and relevant information to make health-centred decisions, the CLEF eHealth lab series has offered shared tasks to the community in the fields of Information Extraction (IE), management, and Information Retrieval (IR) since 2013. These tasks have attracted large participation and led to statistically significant improvements in processing quality. In 2021, CLEF eHealth is calling for participants to contribute to the following two tasks: Task 1 on IE focuses on IE from noisy text. Participants will identify and classify Named Entities in written ultrasonography reports, containing misspellings and inconsistencies, from a major public hospital in Argentina. Identified entities will then have to be classified, which can be very challenging as it requires to handle lexical variations. Task 2 is a novel extension of the most popular and established task on consumer health search (CHS), aiming at retrieving relevant, understandable, and credible information for patients and their next-of-kins. In this paper we describe recent advances in the fields of IE and IR, and the subsequent offerings of this years CLEF eHealth lab challenges.

This paper details materials, methods, results, and analyses of the Consumer Health Search Task of the CLEF eHealth 2021 Evaluation Lab. This task investigates the effectiveness of information retrieval (IR) approaches in providing access to medical information to laypeople. For this a TREC-style evaluation methodology was applied: a shared collection of documents and queries is distributed, participants' runs received, relevance assessments generated, and participants' submissions evaluated. The task generated a new representative web corpus including web pages acquired from a 2021 CommonCrawl and social media content from Twitter and Reddit, along with a new collection of 55 manually generated layperson medical queries and their respective credibility, understandability, and topicality assessments for returned documents. This year's task focused on three subtask: (i) ad-hoc IR, (ii) weakly supervised IR, and (iii) document credibility prediction. In total, 15 runs were submitted to the three subtasks: eight addressed the ad-hoc IR task, three the weakly supervised IR challenge, and 4 the document credibility prediction challenge. As in previous years, the organizers have made data and tools associated with the task available for future research and development.

In this paper, we provide an overview of the ninth annual edition of the CLEF eHealth evaluation lab. CLEF eHealth 2021 continues our evaluation resource building efforts around the easing and support of patients, their next-of-kins, health care professionals, and health scientists in understanding, accessing, and authoring electronic health information in a multilingual setting. The 2021 lab offered two tasks: Task 1 on multilingual Information Extraction (IE), this year extending to a corpus of Spanish radiology reports; and Task 2 on Consumer Health Search (CHS) that builds on the previous year’s Information Retrieval (IR) tasks. In total, 11 teams took part in these tasks (7 in Task 1 on IE and 4 in Task 2 on IR). Herein, we describe the resources created for these tasks and the evaluation methodology adopted, and we provide a brief summary of the participants of this year’s challenges as well as the results obtained. As in previous years, the organizers have made data, tools, and more specific overview papers associated with the lab tasks available for future research and development.

Background: During the COVID-19 pandemic, medical laypersons with symptoms indicative of a COVID-19 infection commonly sought guidance on whether and where to find medical care. Numerous web-based decision support tools (DSTs) have been developed, both by public and commercial stakeholders, to assist their decision making. Though most of the DSTs’ underlying algorithms are similar and simple decision trees, their mode of presentation differs: some DSTs present a static flowchart, while others are designed as a conversational agent, guiding the user through the decision tree’s nodes step-by-step in an interactive manner. Objective: This study aims to investigate whether interactive DSTs provide greater decision support than noninteractive (ie, static) flowcharts. Methods: We developed mock interfaces for 2 DSTs (1 static, 1 interactive), mimicking patient-facing, freely available DSTs for COVID-19-related self-assessment. Their underlying algorithm was identical and based on the Centers for Disease Control and Prevention’s guidelines. We recruited adult US residents online in November 2020. Participants appraised the appropriate social and care-seeking behavior for 7 fictitious descriptions of patients (case vignettes). Participants in the experimental groups received either the static or the interactive mock DST as support, while the control group appraised the case vignettes unsupported. We determined participants’ accuracy, decision certainty (after deciding), and mental effort to measure the quality of decision support. Participants’ ratings of the DSTs’ usefulness, ease of use, trust, and future intention to use the tools served as measures to analyze differences in participants’ perception of the tools. We used ANOVAs and t tests to assess statistical significance. Results: Our survey yielded 196 responses. The mean number of correct assessments was higher in the intervention groups (interactive DST group: mean 11.71, SD 2.37; static DST group: mean 11.45, SD 2.48) than in the control group (mean 10.17, SD 2.00). Decisional certainty was significantly higher in the experimental groups (interactive DST group: mean 80.7%, SD 14.1%; static DST group: mean 80.5%, SD 15.8%) compared to the control group (mean 65.8%, SD 20.8%). The differences in these measures proved statistically significant in t tests comparing each intervention group with the control group (P

In this paper, we provide an overview of the eight annual edition of the Conference and Labs of the Evaluation Forum (CLEF) eHealth evaluation lab. The Conference and Labs of the Evaluation Forum (CLEF) eHealth 2020 continues our development of evaluation tasks and resources since 2012 to address laypeople’s difficulties to retrieve and digest valid and relevant information in their preferred language to make health-centred decisions. This year’s lab advertised two tasks. Task 1 on Information Extraction (IE) was new and focused on automatic clinical coding of diagnosis and procedure the tenth revision of the International Statistical Classification of Diseases and Related Health Problems (ICD10) codes as well as finding the corresponding evidence text snippets for clinical case documents in Spanish. Task 2 on Information Retrieval (IR) was a novel extension of the most popular and established task in the Conference and Labs of the Evaluation Forum (CLEF) eHealth on Consumer Health Search (CHS). In total 55 submissions were made to these tasks. Herein, we describe the resources created for the two tasks and evaluation methodology adopted. We also summarize lab submissions and results. As in previous years, the organizers have made data and tools associated with the lab tasks available for future research anddevelopment. The ongoing substantial community interest in the tasks and their resources has led to the Conference and Labs of the Evaluation Forum (CLEF) eHealth maturing as a primary venue for all interdisciplinary actors of the ecosystem for producing, processing, and consuming electronic healthinformation.

Introduction. The primary aim of this study is to evaluate the temporal correlation between Google Trends and the data on measles infection arising from the conventional surveillance system, reported by the Istituto Superiore di Sanità's (ISS) bulletin. Moreover, this study is also aimed at forecasting the trends of the reported infectious diseases cases over time. Materials and Methods. The reported cases of measles were selected from January 2013 until October 2018. The data on Internet searches have been obtained from Google Trends; the research data referred to the first 48 weeks of year 2017 have been aggregated on a weekly basis. The search volume provided by Google Trends has a relative nature and is calculated as a percentage of query related to a specific term in connection with a determined place and time-frame. The statistical analyses have been performed by using the Spearman's rank correlation coefficient (rho). The statistical significance level for such analyses has been fixed in 0.05. Outcomes. We have observed a strong correlation at a lag of 0 to -4 weeks (rho > 0.70) with the cases reported by ISS with the strongest correlation at a lag of -3 weeks (rho > 0.80 both for measles than for the symptoms of the measles). The database containing monthly data has shown a moderate correlation at a lag of -1 to +1 months and a strongest correlation at a lag of -1 (rho = 0.6152 for measles and rho = 0.5039 for symptoms of the measles). Conclusion. The surveillance systems based on Google Trends have a potential role in public health in order to provide near real-time indicators of the spread of infectious diseases. Therefore the huge potential of this approach could be used in the immediate future as a support of the traditional surveillance systems.

A report on the Genome Informatics conference, held at the Wellcome Genome Campus Conference Centre, Hinxton, United Kingdom, 19-22 September 2016.

SummaryHealthcare processes, especially those belonging to the clinical domain, are acknowledged as complex and characterized by the dynamic nature of the diagnosis, the variability of the decisions made by experts driven by their experiences, the local constraints, the patient’s needs, the uncertainty of the patient’s response, and the indeterminacy of patient’s compliance to treatment. Also, the multiple actors involved in patient’s care need clear and transparent communication to ensure care coordination.In this paper, we propose a methodology to model healthcare processes in order to break out complexity and provide transparency.The model is grounded on a set of requirements that make the healthcare domain unique with respect to other knowledge domains. The modeling methodology is based on three main phases: the study of the environmental context, the conceptual modeling, and the logical modeling.The proposed methodology was validated by applying it to the case study of the rehabilitation process of stroke patients in the specific setting of a specialized rehabilitation center. The resulting model was used to define the specifications of a software artifact for the digital administration and collection of assessment tests that was also implemented.Despite being only an example, our case study showed the ability of process modeling to answer the actual needs in healthcare practices. Independently from the medical domain in which the modeling effort is done, the proposed methodology is useful to create high-quality models, and to detect and take into account relevant and tricky situations that can occur during process execution.

ABCD : Appropriate Blood pressure Control in Diabetes ABI : ankle–brachial index ABPM : ambulatory blood pressure monitoring ACCESS : Acute Candesartan Cilexetil Therapy in Stroke Survival ACCOMPLISH : Avoiding Cardiovascular Events in Combination Therapy in Patients Living with Systolic Hypertension ACCORD : Action to Control Cardiovascular Risk in Diabetes ACE : angiotensin-converting enzyme ACTIVE I : Atrial Fibrillation Clopidogrel Trial with Irbesartan for Prevention of Vascular Events ADVANCE : Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation AHEAD : Action for HEAlth in Diabetes ALLHAT : Antihypertensive and Lipid-Lowering Treatment to Prevent Heart ATtack ALTITUDE : ALiskiren Trial In Type 2 Diabetes Using Cardio-renal Endpoints ANTIPAF : ANgioTensin II Antagonist In Paroxysmal Atrial Fibrillation APOLLO : A Randomized Controlled Trial of Aliskiren in the Prevention of Major Cardiovascular Events in Elderly People ARB : angiotensin receptor blocker ARIC : Atherosclerosis Risk In Communities ARR : aldosterone renin ratio ASCOT : Anglo-Scandinavian Cardiac Outcomes Trial ASCOT-LLA : Anglo-Scandinavian Cardiac Outcomes Trial—Lipid Lowering Arm ASTRAL : Angioplasty and STenting for Renal Artery Lesions A-V : atrioventricular BB : beta-blocker BMI : body mass index BP : blood pressure BSA : body surface area CA : calcium antagonist CABG : coronary artery bypass graft CAPPP : CAPtopril Prevention Project CAPRAF : CAndesartan in the Prevention of Relapsing Atrial Fibrillation CHD : coronary heart disease CHHIPS : Controlling Hypertension and Hypertension Immediately Post-Stroke CKD : chronic kidney disease CKD-EPI : Chronic Kidney Disease—EPIdemiology collaboration CONVINCE : Controlled ONset Verapamil INvestigation of CV Endpoints CT : computed tomography CV : cardiovascular CVD : cardiovascular disease D : diuretic DASH : Dietary Approaches to Stop Hypertension DBP : diastolic blood pressure DCCT : Diabetes Control and Complications Study DIRECT : DIabetic REtinopathy Candesartan Trials DM : diabetes mellitus DPP-4 : dipeptidyl peptidase 4 EAS : European Atherosclerosis Society EASD : European Association for the Study of Diabetes ECG : electrocardiogram EF : ejection fraction eGFR : estimated glomerular filtration rate ELSA : European Lacidipine Study on Atherosclerosis ESC : European Society of Cardiology ESH : European Society of Hypertension ESRD : end-stage renal disease EXPLOR : Amlodipine–Valsartan Combination Decreases Central Systolic Blood Pressure more Effectively than the Amlodipine–Atenolol Combination FDA : U.S. Food and Drug Administration FEVER : Felodipine EVent Reduction study GISSI-AF : Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto Miocardico-Atrial Fibrillation HbA1c : glycated haemoglobin HBPM : home blood pressure monitoring HOPE : Heart Outcomes Prevention Evaluation HOT : Hypertension Optimal Treatment HRT : hormone replacement therapy HT : hypertension HYVET : HYpertension in the Very Elderly Trial IMT : intima-media thickness I-PRESERVE : Irbesartan in Heart Failure with Preserved Systolic Function INTERHEART : Effect of Potentially Modifiable Risk Factors associated with Myocardial Infarction in 52 Countries INVEST : INternational VErapamil SR/T Trandolapril ISH : Isolated systolic hypertension JNC : Joint National Committee JUPITER : Justification for the Use of Statins in Primary Prevention: an Intervention Trial Evaluating Rosuvastatin LAVi : left atrial volume index LIFE : Losartan Intervention For Endpoint Reduction in Hypertensives LV : left ventricle/left ventricular LVH : left ventricular hypertrophy LVM : left ventricular mass MDRD : Modification of Diet in Renal Disease MRFIT : Multiple Risk Factor Intervention Trial MRI : magnetic resonance imaging NORDIL : The Nordic Diltiazem Intervention study OC : oral contraceptive OD : organ damage ONTARGET : ONgoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial PAD : peripheral artery disease PATHS : Prevention And Treatment of Hypertension Study PCI : percutaneous coronary intervention PPAR : peroxisome proliferator-activated receptor PREVEND : Prevention of REnal and Vascular ENdstage Disease PROFESS : Prevention Regimen for Effectively Avoiding Secondary Strokes PROGRESS : Perindopril Protection Against Recurrent Stroke Study PWV : pulse wave velocity QALY : Quality adjusted life years RAA : renin-angiotensin-aldosterone RAS : renin-angiotensin system RCT : randomized controlled trials RF : risk factor ROADMAP : Randomized Olmesartan And Diabetes MicroAlbuminuria Prevention SBP : systolic blood pressure SCAST : Angiotensin-Receptor Blocker Candesartan for Treatment of Acute STroke SCOPE : Study on COgnition and Prognosis in the Elderly SCORE : Systematic COronary Risk Evaluation SHEP : Systolic Hypertension in the Elderly Program STOP : Swedish Trials in Old Patients with Hypertension STOP-2 : The second Swedish Trial in Old Patients with Hypertension SYSTCHINA : SYSTolic Hypertension in the Elderly: Chinese trial SYSTEUR : SYSTolic Hypertension in Europe TIA : transient ischaemic attack TOHP : Trials Of Hypertension Prevention TRANSCEND : Telmisartan Randomised AssessmeNt Study in ACE iNtolerant subjects with cardiovascular Disease UKPDS : United Kingdom Prospective Diabetes Study VADT : Veterans' Affairs Diabetes Trial VALUE : Valsartan Antihypertensive Long-term Use Evaluation WHO : World Health Organization ### 1.1 Principles The 2013 guidelines on hypertension of the European Society of Hypertension (ESH) and the European Society of Cardiology …

Graphical abstractDisplay Omitted Highlight? We present standards in the Healthcare Information System of Lombardy. ? Interoperability guidelines were developed to transmit messages within the region. ? HL7-IHE profiles were implemented to manage messages within single hospitals. ? Citizen's life-long PHR contains CDA release 2 structured documents. ? Standard implementation resulted in a fully interoperable social healthcare system. Information technologies (ITs) have now entered the everyday workflow in a variety of healthcare providers with a certain degree of independence. This independence may be the cause of difficulty in interoperability between information systems and it can be overcome through the implementation and adoption of standards. Here we present the case of the Lombardy Region, in Italy, that has been able, in the last 10years, to set up the Regional Social and Healthcare Information System, connecting all the healthcare providers within the region, and providing full access to clinical and health-related documents independently from the healthcare organization that generated the document itself. This goal, in a region with almost 10 millions citizens, was achieved through a twofold approach: first, the political and operative push towards the adoption of the Health Level 7 (HL7) standard within single hospitals and, second, providing a technological infrastructure for data sharing based on interoperability specifications recognized at the regional level for messages transmitted from healthcare providers to the central domain. The adoption of such regional interoperability specifications enabled the communication among heterogeneous systems placed in different hospitals in Lombardy. Integrating the Healthcare Enterprise (IHE) integration profiles which refer to HL7 standards are adopted within hospitals for message exchange and for the definition of integration scenarios. The IHE patient administration management (PAM) profile with its different workflows is adopted for patient management, whereas the Scheduled Workflow (SWF), the Laboratory Testing Workflow (LTW), and the Ambulatory Testing Workflow (ATW) are adopted for order management. At present, the system manages 4,700,000 pharmacological e-prescriptions, and 1,700,000 e-prescriptions for laboratory exams per month. It produces, monthly, 490,000 laboratory medical reports, 180,000 radiology medical reports, 180,000 first aid medical reports, and 58,000 discharge summaries. Hence, despite there being still work in progress, the Lombardy Region healthcare system is a fully interoperable social healthcare system connecting patients, healthcare providers, healthcare organizations, and healthcare professionals in a large and heterogeneous territory through the implementation of international health standards.

iPad world and Avatar are a couple of really intriguing technological toys waiting for serious and useful research for their proper allocation in the medical arena. To be added to medical and health-care informatics, the adjectives consumer and personalized will drive much of the upcoming research. In addition, the lifelong PHR will generate similar effects, according to its action lines, both institutional and patient at home. Data noise, de materialization, access policies, digital signature, responsibility remapping, and more, all these make a good arrivederci to the 30th anniversary, ten years from now.

Curricular recommendations coming from highly respectable associations are highly useful. Nevertheless, they show fatigue in keeping the pace of any fast evolution, as in the ICT happens. So we do the attempt to disclose the emerging challenges affecting e-Health curricular education.

Integrating patient-generated data into clinical research would improve the reliability of results, especially when longitudinal, chronic, home-based monitoring is needed. To this end, we designed, implemented, and tested a system that allows integrating patient-generated data into electronic case report form (eCRF), using a standards based architecture that ensured the fulfillment of the major requirements for digital data in clinical studies. The system was tested in a clinical investigation for the optimization of deep brain stimulation therapy in patients with Parkinson’s disease that required both the collection of patient-generated data and of clinical and neurophysiological data. The validation showed that the implemented system was able to provide a reliable solution for including the patient as direct digital data source, ensuring reliability, integrity, security, attributability, and auditability of data.