First Measurement of η Meson Production in Neutrino Interactions on Argon with MicroBooNE

We present a measurement of η production from neutrino interactions on argon with the MicroBooNE detector. The modeling of resonant neutrino interactions on argon is a critical aspect of the neutrino oscillation physics program being carried out by the DUNE and Short Baseline Neutrino programs. η production in neutrino interactions provides a powerful new probe of resonant interactions, complementary to pion channels, and is particularly suited to the study of higher-order resonances beyond the Δ(1232). We measure a flux-integrated cross section for neutrino-induced η production on argon of 3.22±0.84(stat)±0.86(syst) 10^{-41}  cm^{2}/nucleon. By demonstrating the successful reconstruction of the two photons resulting from η production, this analysis enables a novel calibration technique for electromagnetic showers in GeV accelerator neutrino experiments.

Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews

BACKGROUND

Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data.

OBJECTIVE

This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework.

METHODS

A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English.

RESULTS

A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions.

CONCLUSIONS

The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.

Search for Heavy Neutral Leptons in Electron-Positron and Neutral-Pion Final States with the MicroBooNE Detector

We present the first search for heavy neutral leptons (HNLs) decaying into νe^{+}e^{-} or νπ^{0} final states in a liquid-argon time projection chamber using data collected with the MicroBooNE detector. The data were recorded synchronously with the NuMI neutrino beam from Fermilab's main injector corresponding to a total exposure of 7.01×10^{20} protons on target. We set upper limits at the 90% confidence level on the mixing parameter |U_{μ4}|^{2} in the mass ranges 10≤m_{HNL}≤150  MeV for the νe^{+}e^{-} channel and 150≤m_{HNL}≤245  MeV for the νπ^{0} channel, assuming |U_{e4}|^{2}=|U_{τ4}|^{2}=0. These limits represent the most stringent constraints in the mass range 35 Collapse

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Affiliation(s)

P Abratenko Tufts University, Medford, Massachusetts 02155, USA
O Alterkait Tufts University, Medford, Massachusetts 02155, USA
D Andrade Aldana Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
L Arellano The University of Manchester, Manchester M13 9PL, United Kingdom
J Asaadi University of Texas, Arlington, Texas 76019, USA
A Ashkenazi Tel Aviv University, Tel Aviv, Israel, 69978
S Balasubramanian Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
B Baller Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
G Barr University of Oxford, Oxford OX1 3RH, United Kingdom
D Barrow University of Oxford, Oxford OX1 3RH, United Kingdom
J Barrow Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA Tel Aviv University, Tel Aviv, Israel, 69978
V Basque Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
O Benevides Rodrigues Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
S Berkman Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA Michigan State University, East Lansing, Michigan 48824, USA
A Bhanderi The University of Manchester, Manchester M13 9PL, United Kingdom
A Bhat University of Chicago, Chicago, Illinois 60637, USA
M Bhattacharya Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
M Bishai Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
A Blake Lancaster University, Lancaster LA1 4YW, United Kingdom
B Bogart University of Michigan, Ann Arbor, Michigan 48109, USA
T Bolton Kansas State University (KSU), Manhattan, Kansas 66506, USA
J Y Book Harvard University, Cambridge, Massachusetts 02138, USA
M B Brunetti University of Warwick, Coventry CV4 7AL, United Kingdom
L Camilleri Columbia University, New York, New York 10027, USA
Y Cao The University of Manchester, Manchester M13 9PL, United Kingdom
D Caratelli University of California, Santa Barbara, California 93106, USA
F Cavanna Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
G Cerati Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
A Chappell University of Warwick, Coventry CV4 7AL, United Kingdom
Y Chen SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
J M Conrad Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
M Convery SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
L Cooper-Troendle University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
J I Crespo-Anadón Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid E-28040, Spain
R Cross University of Warwick, Coventry CV4 7AL, United Kingdom
M Del Tutto Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
S R Dennis University of Cambridge, Cambridge CB3 0HE, United Kingdom
P Detje University of Cambridge, Cambridge CB3 0HE, United Kingdom
A Devitt Lancaster University, Lancaster LA1 4YW, United Kingdom
R Diurba Universität Bern, Bern CH-3012, Switzerland
Z Djurcic Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
R Dorrill Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
K Duffy University of Oxford, Oxford OX1 3RH, United Kingdom
S Dytman University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
B Eberly University of Southern Maine, Portland, Maine 04104, USA
P Englezos Rutgers University, Piscataway, New Jersey 08854, USA
A Ereditato University of Chicago, Chicago, Illinois 60637, USA Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
J J Evans The University of Manchester, Manchester M13 9PL, United Kingdom
R Fine Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
O G Finnerud The University of Manchester, Manchester M13 9PL, United Kingdom
W Foreman Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
B T Fleming University of Chicago, Chicago, Illinois 60637, USA
D Franco University of Chicago, Chicago, Illinois 60637, USA
A P Furmanski University of Minnesota, Minneapolis, Minnesota 55455, USA
F Gao University of California, Santa Barbara, California 93106, USA
D Garcia-Gamez Universidad de Granada, Granada E-18071, Spain
S Gardiner Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
G Ge Columbia University, New York, New York 10027, USA
S Gollapinni Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
E Gramellini The University of Manchester, Manchester M13 9PL, United Kingdom
P Green University of Oxford, Oxford OX1 3RH, United Kingdom
H Greenlee Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
L Gu Lancaster University, Lancaster LA1 4YW, United Kingdom
W Gu Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
R Guenette The University of Manchester, Manchester M13 9PL, United Kingdom
P Guzowski The University of Manchester, Manchester M13 9PL, United Kingdom
L Hagaman University of Chicago, Chicago, Illinois 60637, USA
O Hen Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
C Hilgenberg University of Minnesota, Minneapolis, Minnesota 55455, USA
G A Horton-Smith Kansas State University (KSU), Manhattan, Kansas 66506, USA
Z Imani Tufts University, Medford, Massachusetts 02155, USA
B Irwin University of Minnesota, Minneapolis, Minnesota 55455, USA
M Ismail University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
C James Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
X Ji Nankai University, Nankai District, Tianjin 300071, China
J H Jo Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
R A Johnson University of Cincinnati, Cincinnati, Ohio 45221, USA
Y-J Jwa Columbia University, New York, New York 10027, USA
D Kalra Columbia University, New York, New York 10027, USA
N Kamp Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
G Karagiorgi Columbia University, New York, New York 10027, USA
W Ketchum Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
M Kirby Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
T Kobilarcik Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
I Kreslo Universität Bern, Bern CH-3012, Switzerland
M B Leibovitch University of California, Santa Barbara, California 93106, USA
I Lepetic Rutgers University, Piscataway, New Jersey 08854, USA
J-Y Li University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
K Li Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
Y Li Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
K Lin Rutgers University, Piscataway, New Jersey 08854, USA
B R Littlejohn Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
H Liu Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
W C Louis Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
X Luo University of California, Santa Barbara, California 93106, USA
C Mariani Center for Neutrino Physics, Virginia Tech, Blacksburg, Viriginia 24061, USA
D Marsden The University of Manchester, Manchester M13 9PL, United Kingdom
J Marshall University of Warwick, Coventry CV4 7AL, United Kingdom
N Martinez Kansas State University (KSU), Manhattan, Kansas 66506, USA
D A Martinez Caicedo South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
S Martynenko Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
A Mastbaum Rutgers University, Piscataway, New Jersey 08854, USA
I Mawby University of Warwick, Coventry CV4 7AL, United Kingdom
N McConkey University College London, London WC1E 6BT, United Kingdom
V Meddage Kansas State University (KSU), Manhattan, Kansas 66506, USA
J Micallef Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA Tufts University, Medford, Massachusetts 02155, USA
K Miller University of Chicago, Chicago, Illinois 60637, USA
A Mogan Colorado State University, Fort Collins, Colorado 80523, USA
T Mohayai Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA Indiana University, Bloomington, Indiana 47405, USA
M Mooney Colorado State University, Fort Collins, Colorado 80523, USA
A F Moor University of Cambridge, Cambridge CB3 0HE, United Kingdom
C D Moore Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
L Mora Lepin The University of Manchester, Manchester M13 9PL, United Kingdom
M M Moudgalya The University of Manchester, Manchester M13 9PL, United Kingdom
S Mulleriababu Universität Bern, Bern CH-3012, Switzerland
D Naples University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
A Navrer-Agasson The University of Manchester, Manchester M13 9PL, United Kingdom
N Nayak Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
M Nebot-Guinot University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
J Nowak Lancaster University, Lancaster LA1 4YW, United Kingdom
N Oza Columbia University, New York, New York 10027, USA
O Palamara Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
N Pallat University of Minnesota, Minneapolis, Minnesota 55455, USA
V Paolone University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
A Papadopoulou Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
V Papavassiliou New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
H B Parkinson University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
S F Pate New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
N Patel Lancaster University, Lancaster LA1 4YW, United Kingdom
Z Pavlovic Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
E Piasetzky Tel Aviv University, Tel Aviv, Israel, 69978
I Pophale Lancaster University, Lancaster LA1 4YW, United Kingdom
X Qian Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
J L Raaf Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
V Radeka Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
A Rafique Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
M Reggiani-Guzzo University of Edinburgh, Edinburgh EH9 3FD, United Kingdom The University of Manchester, Manchester M13 9PL, United Kingdom
L Ren New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
L Rochester SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
J Rodriguez Rondon South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
M Rosenberg Tufts University, Medford, Massachusetts 02155, USA
M Ross-Lonergan Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
C Rudolf von Rohr Universität Bern, Bern CH-3012, Switzerland
I Safa Columbia University, New York, New York 10027, USA
G Scanavini Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
D W Schmitz University of Chicago, Chicago, Illinois 60637, USA
A Schukraft Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
W Seligman Columbia University, New York, New York 10027, USA
M H Shaevitz Columbia University, New York, New York 10027, USA
R Sharankova Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
J Shi University of Cambridge, Cambridge CB3 0HE, United Kingdom
E L Snider Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
M Soderberg Syracuse University, Syracuse, New York 13244, USA
S Söldner-Rembold The University of Manchester, Manchester M13 9PL, United Kingdom
J Spitz University of Michigan, Ann Arbor, Michigan 48109, USA
M Stancari Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
J St John Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
T Strauss Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
A M Szelc University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
W Tang University of Tennessee, Knoxville, Tennessee 37996, USA
N Taniuchi University of Cambridge, Cambridge CB3 0HE, United Kingdom
K Terao SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
C Thorpe Lancaster University, Lancaster LA1 4YW, United Kingdom The University of Manchester, Manchester M13 9PL, United Kingdom
D Torbunov Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
D Totani University of California, Santa Barbara, California 93106, USA
M Toups Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
Y-T Tsai SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
J Tyler Kansas State University (KSU), Manhattan, Kansas 66506, USA
M A Uchida University of Cambridge, Cambridge CB3 0HE, United Kingdom
T Usher SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
B Viren Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
M Weber Universität Bern, Bern CH-3012, Switzerland
H Wei Louisiana State University, Baton Rouge, Louisiana 70803, USA
A J White University of Chicago, Chicago, Illinois 60637, USA
S Wolbers Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
T Wongjirad Tufts University, Medford, Massachusetts 02155, USA
M Wospakrik Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
K Wresilo University of Cambridge, Cambridge CB3 0HE, United Kingdom
W Wu University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
E Yandel University of California, Santa Barbara, California 93106, USA
T Yang Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
L E Yates Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
H W Yu Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
G P Zeller Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
J Zennamo Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
C Zhang Brookhaven National Laboratory (BNL), Upton, New York 11973, USA

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Can OpenEHR, ISO 13606, and HL7 FHIR Work Together? An Agnostic Approach for the Selection and Application of Electronic Health Record Standards to the Next-Generation Health Data Spaces

In order to maximize the value of electronic health records (EHRs) for both health care and secondary use, it is necessary for the data to be interoperable and reusable without loss of the original meaning and context, in accordance with the findable, accessible, interoperable, and reusable (FAIR) principles. To achieve this, it is essential for health data platforms to incorporate standards that facilitate addressing needs such as formal modeling of clinical knowledge (health domain concepts) as well as the harmonized persistence, query, and exchange of data across different information systems and organizations. However, the selection of these specifications has not been consistent across the different health data initiatives, often applying standards to address needs for which they were not originally designed. This issue is essential in the current scenario of implementing the European Health Data Space, which advocates harmonization, interoperability, and reuse of data without regulating the specific standards to be applied for this purpose. Therefore, this viewpoint aims to establish a coherent, agnostic, and homogeneous framework for the use of the most impactful EHR standards in the new-generation health data spaces: OpenEHR, International Organization for Standardization (ISO) 13606, and Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). Thus, a panel of EHR standards experts has discussed several critical points to reach a consensus that will serve decision-making teams in health data platform projects who may not be experts in these EHR standards. It was concluded that these specifications possess different capabilities related to modeling, flexibility, and implementation resources. Because of this, in the design of future data platforms, these standards must be applied based on the specific needs they were designed for, being likewise fully compatible with their combined functional and technical implementation.

Electronic health records (EHRs) in clinical research and platform trials: Application of the innovative EHR-based methods developed by EU-PEARL

OBJECTIVE

Electronic Health Record (EHR) systems are digital platforms in clinical practice used to collect patients' clinical information related to their health status and represents a useful storage of real-world data. EHRs have a potential role in research studies, in particular, in platform trials. Platform trials are innovative trial designs including multiple trial arms (conducted simultaneously and/or sequentially) on different treatments under a single master protocol. However, the use of EHRs in research comes with important challenges such as incompleteness of records and the need to translate trial eligibility criteria into interoperable queries. In this paper, we aim to review and to describe our proposed innovative methods to tackle some of the most important challenges identified. This work is part of the Innovative Medicines Initiative (IMI) EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) project's work package 3 (WP3), whose objective is to deliver tools and guidance for EHR-based protocol feasibility assessment, clinical site selection, and patient pre-screening in platform trials, investing in the building of a data-driven clinical network framework that can execute these complex innovative designs for which feasibility assessments are critically important.

METHODS

ISO standards and relevant references informed a readiness survey, producing 354 criteria with corresponding questions selected and harmonised through a 7-round scoring process (0-1) in stakeholder meetings, with 85% of consensus being the threshold of acceptance for a criterium/question. ATLAS cohort definition and Cohort Diagnostics were mainly used to create the trial feasibility eligibility (I/E) criteria as executable interoperable queries.

RESULTS

The WP3/EU-PEARL group developed a readiness survey (eSurvey) for an efficient selection of clinical sites with suitable EHRs, consisting of yes-or-no questions, and a set-up of interoperable proxy queries using physicians' defined trial criteria. Both actions facilitate recruiting trial participants and alignment between study costs/timelines and data-driven recruitment potential.

CONCLUSION

The eSurvey will help create an archive of clinical sites with mature EHR systems suitable to participate in clinical trials/platform trials, and the interoperable proxy queries of trial eligibility criteria will help identify the number of potential participants. Ultimately, these tools will contribute to the production of EHR-based protocol design.

First Double-Differential Measurement of Kinematic Imbalance in Neutrino Interactions with the MicroBooNE Detector

We report the first measurement of flux-integrated double-differential quasielasticlike neutrino-argon cross sections, which have been made using the Booster Neutrino Beam and the MicroBooNE detector at Fermi National Accelerator Laboratory. The data are presented as a function of kinematic imbalance variables which are sensitive to nuclear ground-state distributions and hadronic reinteraction processes. We find that the measured cross sections in different phase-space regions are sensitive to different nuclear effects. Therefore, they enable the impact of specific nuclear effects on the neutrino-nucleus interaction to be isolated more completely than was possible using previous single-differential cross section measurements. Our results provide precision data to help test and improve neutrino-nucleus interaction models. They further support ongoing neutrino-oscillation studies by establishing phase-space regions where precise reaction modeling has already been achieved.

Identification of Drugs Acting as Perpetrators in Common Drug Interactions in a Cohort of Geriatric Patients from Southern Italy and Analysis of the Gene Polymorphisms That Affect Their Interacting Potential

BACKGROUND

Pharmacogenomic factors affect the susceptibility to drug-drug interactions (DDI). We identified drug interaction perpetrators among the drugs prescribed to a cohort of 290 older adults and analysed the prevalence of gene polymorphisms that can increase their interacting potential. We also pinpointed clinical decision support systems (CDSSs) that incorporate pharmacogenomic factors in DDI risk evaluation.

METHODS

Perpetrator drugs were identified using the Drug Interactions Flockhart Table, the DRUGBANK website, and the Mayo Clinic Pharmacogenomics Association Table. Allelic variants affecting their activity were identified with the PharmVar, PharmGKB, dbSNP, ensembl and 1000 genome databases.

RESULTS

Amiodarone, amlodipine, atorvastatin, digoxin, esomperazole, omeprazole, pantoprazole, simvastatin and rosuvastatin were perpetrator drugs prescribed to >5% of our patients. Few allelic variants affecting their perpetrator activity showed a prevalence >2% in the European population: CYP3A4/5*22, *1G, *3, CYP2C9*2 and *3, CYP2C19*17 and *2, CYP2D6*4, *41, *5, *10 and *9 and SLC1B1*15 and *5. Few commercial CDSS include pharmacogenomic factors in DDI-risk evaluation and none of them was designed for use in older adults.

CONCLUSIONS

We provided a list of the allelic variants influencing the activity of drug perpetrators in older adults which should be included in pharmacogenomics-oriented CDSSs to be used in geriatric medicine.

Clinical Research Informatics: Contributions from 2022

OBJECTIVES

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2022.

METHOD

A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.

RESULTS

Among the 1,324 papers returned by the search, published in 2022, that were in the scope of the various areas of CRI, the full review process selected four best papers. The first best paper describes the process undertaken in Germany, under the national Medical Informatics Initiative, to define a process and to gain multi-decision-maker acceptance of broad consent for the reuse of health data for research whilst remaining compliant with the European General Data Protection Regulation. The authors of the second-best paper present a federated architecture for the conduct of clinical trial feasibility queries that utilizes HL7 Fast Healthcare Interoperability Resources and an HL7 standard query representation. The third best paper aligns with the overall theme of this Yearbook, the inclusivity of potential participants in clinical trials, with recommendations to ensure greater equity. The fourth proposes a multi-modal modelling approach for large scale phenotyping from electronic health record information. This year's survey paper has also examined equity, along with data bias, and found that the relevant publications in 2022 have focused almost exclusively on the issue of bias in Artificial Intelligence (AI).

CONCLUSIONS

The literature relevant to CRI in 2022 has largely been dominated by publications that seek to maximise the reusability of wide scale and representative electronic health record information for research, either as big data for distributed analysis or as a source of information from which to identify suitable patients accurately and equitably for invitation to participate in clinical trials.

Digital Analysis of Clinical Screening Criteria for a Rare Disease - Behcet's Disease

The objective is to identify clinical screening criteria for a rare disease,- Behcet's disease and to analyse the digitally structured and unstructured components of the Identified Clinical criteria, build a clinical archetype using OpenEHR editor to be used by learning health support systems for clinical screening of the disease. Methods/Search Strategy: Literature search was conducted, 230 papers were screened, and finally 5 papers were retained, analysed and summarised. Digital Analysis of the clinical criteria was done and a sandardised clinical knowledge model of the same was built using OpenEHR editor, underpinned by OpenEHR international standards. Results The structured and unstructured components of the criteria analysed to be able to incorporate them in a learning health system to screen patients for Behcet's disease. SNOMED CT and Read codes were assigned to the structured componenets. Possible misdiagnosis were identified, along with their corresponding clinical terminology codes that can be incorporated in the Electronic Health Record systems. Conclusion: The identified clinical screening was digitally analysed which can be embedded into a clinical decision support system that can be plugged onto the primary care systems to give an alert to the clinicians if a patient needs to be screened for a rare disease, for e.g., Behcet's.

First Measurement of Quasielastic Λ Baryon Production in Muon Antineutrino Interactions in the MicroBooNE Detector

We present the first measurement of the cross section of Cabibbo-suppressed Λ baryon production, using data collected with the MicroBooNE detector when exposed to the neutrinos from the main injector beam at the Fermi National Accelerator Laboratory. The data analyzed correspond to 2.2×10^{20} protons on target running in neutrino mode, and 4.9×10^{20} protons on target running in anti-neutrino mode. An automated selection is combined with hand scanning, with the former identifying five candidate Λ production events when the signal was unblinded, consistent with the GENIE prediction of 5.3±1.1 events. Several scanners were employed, selecting between three and five events, compared with a prediction from a blinded Monte Carlo simulation study of 3.7±1.0 events. Restricting the phase space to only include Λ baryons that decay above MicroBooNE's detection thresholds, we obtain a flux averaged cross section of 2.0_{-1.7}^{+2.2}×10^{-40}  cm^{2}/Ar, where statistical and systematic uncertainties are combined.

First Constraints on Light Sterile Neutrino Oscillations from Combined Appearance and Disappearance Searches with the MicroBooNE Detector

We present a search for eV-scale sterile neutrino oscillations in the MicroBooNE liquid argon detector, simultaneously considering all possible appearance and disappearance effects within the 3+1 active-to-sterile neutrino oscillation framework. We analyze the neutrino candidate events for the recent measurements of charged-current ν_{e} and ν_{μ} interactions in the MicroBooNE detector, using data corresponding to an exposure of 6.37×10^{20} protons on target from the Fermilab booster neutrino beam. We observe no evidence of light sterile neutrino oscillations and derive exclusion contours at the 95% confidence level in the plane of the mass-squared splitting Δm_{41}^{2} and the sterile neutrino mixing angles θ_{μe} and θ_{ee}, excluding part of the parameter space allowed by experimental anomalies. Cancellation of ν_{e} appearance and ν_{e} disappearance effects due to the full 3+1 treatment of the analysis leads to a degeneracy when determining the oscillation parameters, which is discussed in this Letter and will be addressed by future analyses.

Clinical Research Informatics

OBJECTIVES

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2021.

METHOD

Using PubMed, we did a bibliographic search using a combination of MeSH descriptors and free-text terms on CRI, followed by a double-blind review in order to select a list of candidate best papers to be peer-reviewed by external reviewers. After peer-review ranking, three section editors met for a consensus meeting and the editorial team was organized to finally conclude on the selected three best papers.

RESULTS

Among the 1,096 papers (published in 2021) returned by the search and in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes an operational and scalable framework for generating EHR datasets based on a detailed clinical model with an application in the domain of the COVID-19 pandemics. The authors of the second best paper present a secure and scalable platform for the preprocessing of biomedical data for deep data-driven health management applied for the detection of pre-symptomatic COVID-19 cases and for biological characterization of insulin-resistance heterogeneity. The third best paper provides a contribution to the integration of care and research activities with the REDCap Clinical Data and Interoperability sServices (CDIS) module improving the accuracy and efficiency of data collection.

CONCLUSIONS

The COVID-19 pandemic is still significantly stimulating research efforts in the CRI field to improve the process deeply and widely for conducting real-world studies as well as for optimizing clinical trials, the duration and cost of which are constantly increasing. The current health crisis highlights the need for healthcare institutions to continue the development and deployment of Big Data spaces, to strengthen their expertise in data science and to implement efficient data quality evaluation and improvement programs.

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.

An Integrated Care Platform System (C3-Cloud) for Care Planning, Decision Support, and Empowerment of Patients With Multimorbidity: Protocol for a Technology Trial

Background

There is an increasing need to organize the care around the patient and not the disease, while considering the complex realities of multiple physical and psychosocial conditions, and polypharmacy. Integrated patient-centered care delivery platforms have been developed for both patients and clinicians. These platforms could provide a promising way to achieve a collaborative environment that improves the provision of integrated care for patients via enhanced information and communication technology solutions for semiautomated clinical decision support.

Objective

The Collaborative Care and Cure Cloud project (C3-Cloud) has developed 2 collaborative computer platforms for patients and members of the multidisciplinary team (MDT) and deployed these in 3 different European settings. The objective of this study is to pilot test the platforms and evaluate their impact on patients with 2 or more chronic conditions (diabetes mellitus type 2, heart failure, kidney failure, depression), their informal caregivers, health care professionals, and, to some extent, health care systems.

Methods

This paper describes the protocol for conducting an evaluation of user experience, acceptability, and usefulness of the platforms. For this, 2 “testing and evaluation” phases have been defined, involving multiple qualitative methods (focus groups and surveys) and advanced impact modeling (predictive modeling and cost-benefit analysis). Patients and health care professionals were identified and recruited from 3 partnering regions in Spain, Sweden, and the United Kingdom via electronic health record screening.

Results

The technology trial in this 4-year funded project (2016-2020) concluded in April 2020. The pilot technology trial for evaluation phases 3 and 4 was launched in November 2019 and carried out until April 2020. Data collection for these phases is completed with promising results on platform acceptance and socioeconomic impact. We believe that the phased, iterative approach taken is useful as it involves relevant stakeholders at crucial stages in the platform development and allows for a sound user acceptance assessment of the final product.

Conclusions

Patients with multiple chronic conditions often experience shortcomings in the care they receive. It is hoped that personalized care plan platforms for patients and collaboration platforms for members of MDTs can help tackle the specific challenges of clinical guideline reconciliation for patients with multimorbidity and improve the management of polypharmacy. The initial evaluative phases have indicated promising results of platform usability. Results of phases 3 and 4 were methodologically useful, yet limited due to the COVID-19 pandemic.

Trial Registration

ClinicalTrials.gov NCT03834207; https://clinicaltrials.gov/ct2/show/NCT03834207

International Registered Report Identifier (IRRID)

RR1-10.2196/21994

Search for an Excess of Electron Neutrino Interactions in MicroBooNE Using Multiple Final-State Topologies

We present a measurement of ν_{e} interactions from the Fermilab Booster Neutrino Beam using the MicroBooNE liquid argon time projection chamber to address the nature of the excess of low energy interactions observed by the MiniBooNE Collaboration. Three independent ν_{e} searches are performed across multiple single electron final states, including an exclusive search for two-body scattering events with a single proton, a semi-inclusive search for pionless events, and a fully inclusive search for events containing all hadronic final states. With differing signal topologies, statistics, backgrounds, reconstruction algorithms, and analysis approaches, the results are found to be either consistent with or modestly lower than the nominal ν_{e} rate expectations from the Booster Neutrino Beam and no excess of ν_{e} events is observed.

Aggregations of Substance in Virtual Drug Models Based on ISO/CEN Standards for Identification of Medicinal Products (IDMP)

In this study representation of chemical substances in IDMP is reviewed, with an exploration of aggregation levels for substance used in the virtual drug data models of RxNorm, SNOMED-CT, ATC/INN, and the Belgian SAM database, for products with a single substance and combinations of substances. Active moiety and available solid states forms are explored for diclofenac, amoxicillin, carbamazepine, amlodipine, with regard to their representation in coding systems such as WHODrug, SMS, UNII, CAS, and SNOMED-CT. By counting the number of medicinal products in Belgium for amlodipine in each level of aggregation, concepts for grouper of substances and two levels of grouper of medicinal products are illustrated. Recommendations are made for the further development of IDMP and its link to international drug classifications.

First Measurement of Energy-Dependent Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector

We report a measurement of the energy-dependent total charged-current cross section σ(E_{ν}) for inclusive muon neutrinos scattering on argon, as well as measurements of flux-averaged differential cross sections as a function of muon energy and hadronic energy transfer (ν). Data corresponding to 5.3×10^{19} protons on target of exposure were collected using the MicroBooNE liquid argon time projection chamber located in the Fermilab booster neutrino beam with a mean neutrino energy of approximately 0.8 GeV. The mapping between the true neutrino energy E_{ν} and reconstructed neutrino energy E_{ν}^{rec} and between the energy transfer ν and reconstructed hadronic energy E_{had}^{rec} are validated by comparing the data and Monte Carlo (MC) predictions. In particular, the modeling of the missing hadronic energy and its associated uncertainties are verified by a new method that compares the E_{had}^{rec} distributions between data and a MC prediction after constraining the reconstructed muon kinematic distributions, energy, and polar angle to those of data. The success of this validation gives confidence that the missing energy in the MicroBooNE detector is well modeled and underpins first-time measurements of both the total cross section σ(E_{ν}) and the differential cross section dσ/dν on argon.

Success Factors for Scaling Up the Adoption of Digital Therapeutics Towards the Realization of P5 Medicine

Introduction

Digital therapeutics (DTx) can be a valuable contribution to the successful scale up of P5 Medicine (personalized, participatory, predictive, preventive, precision medicine) as they offer powerful means of delivering personalization and active patient participation in disease self-management. We investigated how the approval and adoption of DTx within health systems have been approached in five selected European countries and regions, with a view to proposing success factors scaling up their adoption.

Methodology

Preliminary research established best countries or region candidates as being Germany, UK, France, Belgium, and the Spanish Region of Catalonia. The research was informed by a literature review, interviews with public bodies and industry, and a multi-stakeholder workshop to validate the findings and fill in existing gaps.

Results

To authorize the use of digital technologies, the countries and regions passed legislation and developed policy instruments, appointed bodies to assess and certify the products and formalized mechanisms for permitting reimbursement. While DTx is not a commonly used nomenclature, there are digital health technology types defined that have similar requirements as DTx. Assessment and certification frameworks are usually built around the Medical Device Regulation with additional criteria. Reimbursement considerations often observe reimbursement of therapeutic devices and/or medicines. To be integrated into reimbursement systems, countries require manufacturers to demonstrate clinical value and cost-effectiveness. As there are currently very few DTx approved in practice, there is resistance toward clinical acceptance and organizational change, and change management is highly needed to integrate DTx into healthcare systems. The integration and secondary use of DTx data is not encountered in daily practice. Although some enablers exist, there remain technical and legal barriers.

Discussion

DTx strategies should be considered as an integral part of digital health strategies and legislation, and specific DTx pathways with clear and transparent assessment and guidelines that balance regulation and innovation should be defined. To help manufacturers, countries should recommend and list methods that are widely accepted and ensure scientific robustness, aligned to the MDR requirements to support transfer of relevant and comparable data across countries. To facilitate rapid uptake of innovation, countries should add flexibility to the framework by allowing temporary market authorization to enable data collection that can support the clinical and socio-economic evaluation and data gathering phase. Certification should trigger rapid price setting and reimbursement mechanisms, and dynamic ways to adjust price and reimbursement levels in time should be established. Relevant stakeholders should be approached on the potential impacts of DTx through transparent communication and change management strategies should be considered. These findings should be validated with a wider range of stakeholders.

Search for Neutrino-Induced Neutral-Current Δ Radiative Decay in MicroBooNE and a First Test of the MiniBooNE Low Energy Excess under a Single-Photon Hypothesis

We report results from a search for neutrino-induced neutral current (NC) resonant Δ(1232) baryon production followed by Δ radiative decay, with a ⟨0.8⟩  GeV neutrino beam. Data corresponding to MicroBooNE's first three years of operations (6.80×10^{20} protons on target) are used to select single-photon events with one or zero protons and without charged leptons in the final state (1γ1p and 1γ0p, respectively). The background is constrained via an in situ high-purity measurement of NC π^{0} events, made possible via dedicated 2γ1p and 2γ0p selections. A total of 16 and 153 events are observed for the 1γ1p and 1γ0p selections, respectively, compared to a constrained background prediction of 20.5±3.65(syst) and 145.1±13.8(syst) events. The data lead to a bound on an anomalous enhancement of the normalization of NC Δ radiative decay of less than 2.3 times the predicted nominal rate for this process at the 90% confidence level (C.L.). The measurement disfavors a candidate photon interpretation of the MiniBooNE low-energy excess as a factor of 3.18 times the nominal NC Δ radiative decay rate at the 94.8% C.L., in favor of the nominal prediction, and represents a greater than 50-fold improvement over the world's best limit on single-photon production in NC interactions in the sub-GeV neutrino energy range.

Factors Affecting Citizen Trust and Public Engagement Relating to the Generation and Use of Real-World Evidence in Healthcare

The potential for the use of real-world data (RWD) to generate real-world evidence (RWE) that can inform clinical decision-making and health policy is increasingly recognized, albeit with hesitancy in some circles. If used appropriately, the rapidly expanding wealth of health data could improve healthcare research, delivery of care, and patient outcomes. However, this depends on two key factors: (1) building structures that increase the confidence and willingness of European Union (EU) citizens to permit the collection and use of their data, and (2) development of EU health policy to support and shape data collection infrastructures, methodologies, transmission, and use. The great potential for use of RWE in healthcare improvement merits careful exploration of the drivers of, and challenges preventing, efficient RWD curation. Literature-based research was performed to identify relevant themes and discussion topics for two sets of expert panels, organized by the European Alliance for Personalised Medicine. These expert panels discussed steps that would enable a gradual but steady growth in the quantity, quality, and beneficial deployment of RWE. Participants were selected to provide insight based on their professional medical, economic, patient, industry, or governmental experience. Here, we propose a framework that addresses public trust and access to data, cross-border governance, alignment of evidence frameworks, and demonstrable improvements in healthcare decisions. We also discuss key case studies that support these recommendations, in accordance with the discussions at the expert panels.

Search for Active-Sterile Antineutrino Mixing Using Neutral-Current Interactions with the NOvA Experiment

This Letter reports results from the first long-baseline search for sterile antineutrinos mixing in an accelerator-based antineutrino-dominated beam. The rate of neutral-current interactions in the two NOvA detectors, at distances of 1 and 810 km from the beam source, is analyzed using an exposure of 12.51×10^{20} protons-on-target from the NuMI beam at Fermilab running in antineutrino mode. A total of 121 of neutral-current candidates are observed at the far detector, compared to a prediction of 122±11(stat.)±15(syst.) assuming mixing only between three active flavors. No evidence for ν[over ¯]_{μ}→ν[over ¯]_{s} oscillation is observed. Interpreting this result within a 3+1 model, constraints are placed on the mixing angles θ_{24}<25° and θ_{34}<32° at the 90% C.L. for 0.05  eV^{2}≤Δm_{41}^{2}≤0.5  eV^{2}, the range of mass splittings that produces no significant oscillations at the near detector. These are the first 3+1 confidence limits set using long-baseline accelerator antineutrinos.

EU-PEARL: Changing the paradigm of clinical trials in Europe

Issue

Recently, innovative clinical trial designs have been proposed, which have the potential to revolutionize clinical research. Whereas classical trials mostly evaluate only one investigational drug, platform trials embed various trials under a shared master protocol to enable the evaluation of multiple interventions for a disease or condition. Platform trials have mostly been used to evaluate cancer therapies, but also recently for COVID-19. The EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) project aims to expand the use of platform trials as the backbone of drug development.

Description of Issue

EU-PEARL is a Innovative Medicines Initiative 2 Joint Undertaking funded project (2019-2023), and a strategic public and private sector alliance, which aims to support the transformation of the classical trial approach into a cross-company collaborative, multi-compound platform, centred around patients, not diseases. How to operationalize and sustain this? A multi-stakeholder, mixed-methods approach is taken, focused around the concept of an integrated research platform (IRP), i.e., a common enabling framework for platform trials. The IRP will be established as a sustainable and scalable global solution, consisting of an infrastructure, workflows, and guidance on how to meet complex regulatory, ethical, legal, statistical and data requirements. Lessons from COVID-19 trials will be incorporated.

Results

A disease-agnostic IRP is being developed, as well as four disease specific IRPs in four areas of high public health relevance, i.e. major depressive disorder, tuberculosis, non-alcoholic steatohepatitis, and neurofibromatosis.

Lessons

Multi-stakeholder, multi-sector and multi-disciplinary collaborations are challenging in practice, but rewarding in outcomes. Given the complexity of setting-up these novel trials, clear communication and standardized terminology must be established, as well as continual awareness building of their components, challenges and benefits.

Key messages

EU-PEARL intends to stage the clinical trials of the future, which will be more adaptive, efficient and patient-centred by design and outcome. EU-PEAR&Lacute;s efforts aim to contribute to timely societal access to affordable medicines and address unmet health needs.

Key Contributions in Clinical Research Informatics

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2020.

Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between two section editors and the editorial team was organized to finally conclude on the selected four best papers.

Results: Among the 877 papers published in 2020 and returned by the search, there were four best papers selected. The first best paper describes a method for mining temporal sequences from clinical documents to infer disease trajectories and enhancing high-throughput phenotyping. The authors of the second best paper demonstrate that the generation of synthetic Electronic Health Record (EHR) data through Generative Adversarial Networks (GANs) could be substantially improved by more appropriate training and evaluation criteria. The third best paper offers an efficient advance on methods to detect adverse drug events by computer-assisting expert reviewers with annotated candidate mentions in clinical documents. The large-scale data quality assessment study reported by the fourth best paper has clinical research informatics implications, in terms of the trustworthiness of inferences made from analysing electronic health records.

Conclusions: The most significant research efforts in the CRI field are currently focusing on data science with active research in the development and evaluation of Artificial Intelligence/Machine Learning (AI/ML) algorithms based on ever more intensive use of real-world data and especially EHR real or synthetic data. A major lesson that the coronavirus disease 2019 (COVID-19) pandemic has already taught the scientific CRI community is that timely international high-quality data-sharing and collaborative data analysis is absolutely vital to inform policy decisions.

Cosmic Ray Background Removal With Deep Neural Networks in SBND

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

Quality of Hospital Electronic Health Record (EHR) Data Based on the International Consortium for Health Outcomes Measurement (ICHOM) in Heart Failure: Pilot Data Quality Assessment Study

Background

There is increasing recognition that health care providers need to focus attention, and be judged against, the impact they have on the health outcomes experienced by patients. The measurement of health outcomes as a routine part of clinical documentation is probably the only scalable way of collecting outcomes evidence, since secondary data collection is expensive and error-prone. However, there is uncertainty about whether routinely collected clinical data within electronic health record (EHR) systems includes the data most relevant to measuring and comparing outcomes and if those items are collected to a good enough data quality to be relied upon for outcomes assessment, since several studies have pointed out significant issues regarding EHR data availability and quality.

Objective

In this paper, we first describe a practical approach to data quality assessment of health outcomes, based on a literature review of existing frameworks for quality assessment of health data and multistakeholder consultation. Adopting this approach, we performed a pilot study on a subset of 21 International Consortium for Health Outcomes Measurement (ICHOM) outcomes data items from patients with congestive heart failure.

Methods

All available registries compatible with the diagnosis of heart failure within an EHR data repository of a general hospital (142,345 visits and 12,503 patients) were extracted and mapped to the ICHOM format. We focused our pilot assessment on 5 commonly used data quality dimensions: completeness, correctness, consistency, uniqueness, and temporal stability.

Results

We found high scores (>95%) for the consistency, completeness, and uniqueness dimensions. Temporal stability analyses showed some changes over time in the reported use of medication to treat heart failure, as well as in the recording of past medical conditions. Finally, the investigation of data correctness suggested several issues concerning the characterization of missing data values. Many of these issues appear to be introduced while mapping the IMASIS-2 relational database contents to the ICHOM format, as the latter requires a level of detail that is not explicitly available in the coded data of an EHR.

Conclusions

Overall, results of this pilot study revealed good data quality for the subset of heart failure outcomes collected at the Hospital del Mar. Nevertheless, some important data errors were identified that were caused by fundamentally different data collection practices in routine clinical care versus research, for which the ICHOM standard set was originally developed. To truly examine to what extent hospitals today are able to routinely collect the evidence of their success in achieving good health outcomes, future research would benefit from performing more extensive data quality assessments, including all data items from the ICHOM standards set and across multiple hospitals.

Impact of patient access to their electronic health record: systematic review

Patient access to their own electronic health records (EHRs) is likely to become an integral part of healthcare systems worldwide. It has the potential to decrease the healthcare provision costs, improve access to healthcare data, self-care, quality of care, and health and patient-centered outcomes. This systematic literature review is aimed at identifying the impact in terms of benefits and issues that have so far been demonstrated by providing patients access to their own EHRs, via providers' secure patient portals from primary healthcare centers and hospitals. Searches were conducted in PubMed, MEDLINE, CINHAL, and Google scholar. Over 2000 papers were screened and were filtered based on duplicates, then by reading the titles and finally based on their abstracts or full text. In total, 74 papers were retained, analyzed, and summarized. Papers were included if providing patient access to their own EHRs was the primary intervention used in the study and its impact or outcome was evaluated. The search technique used to identify relevant literature for this paper involved input from five experts. While findings from 54 of the 74 papers showed positive outcome or benefits of patient access to their EHRs via patient portals, 10 papers have highlighted concerns, 8 papers have highlighted both and 2 have highlighted absence of negative outcomes. The benefits range from re-assurance, reduced anxiety, positive impact on consultations, better doctor-patient relationship, increased awareness and adherence to medication, and improved patient outcomes (e.g., improving blood pressure and glycemic control in a range of study populations). In addition, patient access to their health information was found to improve self-reported levels of engagement or activation related to self-management, enhanced knowledge, and improve recovery scores, and organizational efficiencies in a tertiary level mental health care facility. However, three studies did not find any statistically significant effect of patient portals on health outcomes. The main concerns have been around security, privacy and confidentiality of the health records, and the anxiety it may cause amongst patients. This literature review identified some benefits, concerns, and attitudes demonstrated by providing patients' access to their own EHRs. This access is often part of government strategies when developing patient-centric self-management elements of a sustainable healthcare system. The findings of this review will give healthcare providers a framework to analyze the benefits offered by promoting patient access to EHRs and decide on the best approach for their own specialties and clinical setup. A robust cost-benefit evaluation of such initiatives along with its impact on major stakeholders within the healthcare system would be essential in understanding the overall impact of such initiatives. Implementation of patient access to their EHRs could help governments to appropriately prioritize the development or adoption of national standards, whilst taking care of local variations and fulfilling the healthcare needs of the population, e.g., UK Government is aiming to make full primary care records available online to every patient. Ultimately, increasing transparency and promoting personal responsibility are key elements of a sustainable healthcare system for future generations.

Metabolomics: The Stethoscope for the Twenty-First Century

Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.

Multiple Sclerosis Data Alliance - A global multi-stakeholder collaboration to scale-up real world data research

The Multiple Sclerosis Data Alliance (MSDA), a global multi-stakeholder collaboration, is working to accelerate research insights for innovative care and treatment for people with multiple sclerosis (MS) through better use of real-world data (RWD). Despite the increasing reliance on RWD, challenges and limitations complicate the generation, collection, and use of these data. MSDA aims to tackle sociological and technical challenges arising with scaling up RWD, specifically focused on MS data. MSDA envisions a patient-centred data ecosystem in which all stakeholders contribute and use big data to co-create the innovations needed to advance timely treatment and care of people with MS.

Digitalisation and COVID-19: The Perfect Storm

“A ship in the harbour is safe, but that is not what ships are built for,” observed that sage 19th century philosopher William Shedd. In other words, technology of high potential is of little value if the potential is not exploited. As the shape of 2020 is increasingly defined by the coronavirus pandemic, digitalisation is like a ship loaded with technology that has a huge capacity for transforming mankind's combat against infectious disease. But it is still moored safely in harbour. Instead of sailing bravely into battle, it remains at the dockside, cowering from the storm beyond the breakwaters. Engineers and fitters constantly fine-tune it, and its officers and deckhands perfect their operating procedures, but that promise is unfulfilled, restrained by the hesitancy and indecision of officialdom. Out there, the seas of the pandemic are turbulent and uncharted, and it is impossible to know in advance everything of the other dangers that may lurk beyond those cloudy horizons. However, the more noble course is for orders to be given to complete the preparations, to cast off and set sail, and to join other vessels crewed by valiant healthcare workers and tireless researchers, already deeply engaged in a rescue mission for the whole of the human race. It is the destiny of digitalisation to navigate those oceans alongside other members of that task force, and the hour of destiny has arrived. This article focuses on the potential enablers and recommendation to maximise learnings during the era of COVID-19.

Bringing Greater Accuracy to Europe's Healthcare Systems: The Unexploited Potential of Biomarker Testing in Oncology

Rapid and continuing advances in biomarker testing are not being matched by take-up in health systems, and this is hampering both patient care and innovation. It also risks costing health systems the opportunity to make their services more efficient and, over time, more economical. This paper sets out the potential of biomarker testing, the unfolding precision and range of possible diagnosis and prediction, and the many obstacles to adoption. It offers case studies of biomarker testing in breast, ovarian, prostate, lung, thyroid and colon cancers, and derives specific lessons as to the potential and actual use of each of them. It also draws lessons about how to improve access and alignment, and to remedy the data deficiencies that impede development. And it suggests solutions to outstanding issues - notably including funding and the tangled web of obtaining reimbursement or equivalent coverage that Europe's fragmented health system implies. It urges a European evolution towards an initial minimum testing scenario, which would guarantee universal access to a suite of biomarker tests for the currently most common conditions, and, further into the future, to an optimum testing scenario in which a much wider range of biomarker tests would be introduced and become part of a more sophisticated health system articulated around personalised medicine. For exploiting genomics to the full, it argues the need for a new policy framework for Europe. Biomarker testing is not an issue that can be treated in isolation, since the purpose of testing is to improve health. Its use is therefore always closely linked to specific health challenges and needs to be viewed in the broader policy context in the EU and more widely. The paper is the result of extensive engagement with experts and decision makers to develop the framework, and consequently represents a wide consensus of views on how healthcare systems should respond from push and pull factors at local, national and cross-border and EU level. It contains strong views and clear recommendations springing from the convictions of patients, clinicians, academics, medicines authorities, HTA bodies, payers, the diagnostic, pharmaceutical and ICT industries, and national policy makers.

Information and Communications Technology-Based Interventions Targeting Patient Empowerment: Framework Development

Background

Empowerment of patients is often an explicit goal of various information and communications technology (ICT) (electronic, digital) interventions where the patients themselves use ICT tools via the internet. Although several models of empowerment exist, a comprehensive and pragmatic framework is lacking for the development of such interventions.

Objective

This study proposes a framework for digital interventions aiming to empower patients that includes a methodology that links objectives, strategies, and evaluation.

Methods

This study is based on a literature review and iterated expert discussions including a focus group to formulate the proposed model. Our model is based on a review of various models of empowerment and models of technology intervention.

Results

Our framework includes the core characteristics of the empowerment concept (control, psychological coping, self-efficacy, understanding, legitimacy, and support) as well as a set of empowerment consequences: expressed patient perceptions, behavior, clinical outcomes, and health systems effects. The framework for designing interventions includes strategies to achieve empowerment goals using different ICT services. Finally, the intervention model can be used to define project evaluations where the aim is to demonstrate empowerment. The study also included example indicators and associated measurement instruments.

Conclusions

This framework, which includes definitions, can be useful for the design and evaluation of digital interventions targeting patient empowerment and assist in the development of methods to measure results in this dimension. Further evaluation in the form of interventional studies will be needed to assess the generalizability of the model.

Clinical Research Informatics

Objectives : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2019.

Method : A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.

Results : Among the 517 papers, published in 2019, returned by the search, that were in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes the use of a homomorphic encryption technique to enable federated analysis of real-world data while complying more easily with data protection requirements. The authors of the second best paper demonstrate the evidence value of federated data networks reporting a large real world data study related to the first line treatment for hypertension. The third best paper reports the migration of the US Food and Drug Administration (FDA) adverse event reporting system database to the OMOP common data model. This work opens the combined analysis of both spontaneous reporting system and electronic health record (EHR) data for pharmacovigilance.

Conclusions : The most significant research efforts in the CRI field are currently focusing on real world evidence generation and especially the reuse of EHR data. With the progress achieved this year in the areas of phenotyping, data integration, semantic interoperability, and data quality assessment, real world data is becoming more accessible and reusable. High quality data sets are key assets not only for large scale observational studies or for changing the way clinical trials are conducted but also for developing or evaluating artificial intelligence algorithms guiding clinical decision for more personalized care. And lastly, security and confidentiality, ethical and regulatory issues, and more generally speaking data governance are still active research areas this year.

Feasibility of Using EN 13606 Clinical Archetypes for Defining Computable Phenotypes

INTRODUCTION

Computable phenotypes are gaining importance as structured and reproducible method of using electronic health data to identify people with certain clinical conditions. A formal standard is not available for defining and formally representing phenotyping algorithms. In this paper, we have tried to build a formal representation of such phenotyping algorithm.

METHODS

We built EN 13606 EHR standard for building clinical archetypes to represent the computable phenotyping algorithm for 'diagnosis of cardiac failure'. As part of this work, we created a set of new clinical archetypes for defining 'cardiac failure diagnosis'. The EN13606 editor called Object Dictionary Client was used which was in-house developed by University College London. We evaluated the ability of EN 13606 to provide clinical archetypes to define EHR phenotyping algorithms using the predefined desiderata for the purpose [Mo et al].

RESULTS

EN 13606 archetypes could represent phenotype components grouped and nested based on their logical meaning. It was possible to build the EHR phenotyping algorithm with the clinical elements and their interrelationships along with hierarchical structure and temporal criteria. But the specific mathematical calculation and temporal relations involved in the algorithm was difficult to incorporate. These will need to be coded and integrated within the clinical information system. These archetypes can be mapped for comparison with the openEHR models. Binding to external clinical terminology is fully supported. However, it does not satisfy all the desiderata defined by Mo et al. A possible way could be an approach using phenotype ontologies and its architectural representation integrated with ISO interoperability.

CONCLUSION

The EN13606 archetypes can be used to define the phenotype algorithm that basically identifies patients by a set of clinical characteristics in their records. Phenotype representations defined in EN 13606 do not satisfy all the desiderata proposed by Mo et al. and thus currently has a limited ability to define the computable phenotyping algorithms. Further work is required to make the EN13606 standard to fully support the objective.

Localisation, Personalisation and Delivery of Best Practice Guidelines on an Integrated Care and Cure Cloud Architecture: The C3-Cloud Approach to Managing Multimorbidity

BACKGROUND

C3-Cloud is an integrated care ICT infrastructure offering seamless patient-centered approach to managing multimorbidity, deployed in three European pilot sites. Challenge: The digital delivery of best practice guidelines unified for multimorbidity, customized to local practice, offering the capability to improve patient personalization and benefit.

METHOD

C3-Cloud has adopted a co-production approach to developing unified multimorbidity guidelines, by collating and reconciling best practice guidelines for each condition. Clinical and technical teams at pilot sites and the C3-Cloud consortium worked in tandem to create the specification and technical implementation.

RESULTS

C3-Cloud offers CDSS for diabetes, renal failure, depression and congenital heart failure, with over 300 rules and checks that deliver four best practice guidelines in parallel, customized for each pilot site.

CONCLUSIONS

The process provided a traceable, maintainable and audited digitally delivered collated and reconciled guidelines.

Propelling Health Care into the Twenties

The scope and potential of personalised health care are underappreciated and underrealised, often because of resistance to change. The consequence is that many inadequacies of health care in Europe persist unnecessarily, and many opportunities for improvement are neglected. This article identifies the principal challenges, outlines possible approaches to resolving them, and highlights the benefits that could result from greater adoption of personalised health care. It locates the discussion in the context of European policy, focusing particularly on the most recent and authoritative reviews of health care in the EU Member States, and on the newly acquired spirit of readiness and pragmatism among European officials to embrace change and innovative technologies in a new decade. It highlights the attention now being given by policymakers to incentives, innovation, and investment as levers to improve European citizens' prospects in a rapidly evolving world, and how these distinct and disruptive themes contribute to a renaissance in thinking about delivering optimal health care in Europe. It explores the chances offered to patients by specific initiatives in health domains such as cancer and antimicrobial resistance, and by innovative science, novel therapies, earlier diagnosis tools, and deeper understanding of health promotion and prevention. And it reflects on how health care providers could benefit from a shift towards better primary care and towards deploying health data more effectively, including the use of artificial intelligence, coupled with a move to a smoother organisational/regulatory structure and realigned professional responsibilities. The conclusion is that preparing Europe's health care systems for the inevitable strains of the coming years is both possible and necessary. A more courageous approach to embracing personalised health care could guarantee the sustainability of Europe's health care systems before rising demands and exponential costs overwhelm them - an exercise in future-proofing, in ensuring that they are equipped to withstand whatever lies ahead. A focus on the potential and implementation of personalised care would permit more efficient use of resources and deliver better quality health-preserving care.

First measurement of neutrino oscillation parameters using neutrinos and antineutrinos by NOvA

The NOvA experiment has seen a 4.4σ signal of ν[over ¯]_{e} appearance in a 2 GeV ν[over ¯]_{μ} beam at a distance of 810 km. Using 12.33×10^{20} protons on target delivered to the Fermilab NuMI neutrino beamline, the experiment recorded 27 ν[over ¯]_{μ}→ν[over ¯]_{e} candidates with a background of 10.3 and 102 ν[over ¯]_{μ}→ν[over ¯]_{μ} candidates. This new antineutrino data are combined with neutrino data to measure the parameters |Δm_{32}^{2}|=2.48_{-0.06}^{+0.11}×10^{-3}  eV^{2}/c^{4} and sin^{2}θ_{23} in the ranges from (0.53-0.60) and (0.45-0.48) in the normal neutrino mass hierarchy. The data exclude most values near δ_{CP}=π/2 for the inverted mass hierarchy by more than 3σ and favor the normal neutrino mass hierarchy by 1.9σ and θ_{23} values in the upper octant by 1.6σ.

Clinical Research Informatics: Contributions from 2018

OBJECTIVES

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2018.

METHOD

A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting of the editorial team was organized to conclude on the selection of best papers.

RESULTS

Among the 1,469 retrieved papers published in 2018 in the various areas of CRI, the full review process selected four best papers. The first best paper describes a simple algorithm detecting co-morbidities in Electronic Healthcare Records (EHRs) using a clinical data warehouse and a knowledge base. The authors of the second best paper present a federated algorithm for predicting heart failure hospital admissions based on patients' medical history described in their distributed EHRs. The third best paper reports the evaluation of an open source, interoperable, and scalable data quality assessment tool measuring completeness of data items, which can be run on different architectures (EHRs and Clinical Data Warehouses (CDWs) based on PCORnet or OMOP data models). The fourth best paper reports a data quality program conducted across 37 hospitals addressing data quality Issues through the whole data life cycle from patient to researcher.

CONCLUSIONS

Research efforts in the CRI field currently focus on consolidating promises of early Distributed Research Networks aimed at maximizing the potential of large-scale, harmonized data from diverse, quickly developing digital sources. Data quality assessment methods and tools as well as privacy-enhancing techniques are major concerns. It is also notable that, following examples in the US and Asia, ambitious regional or national plans in Europe are launched that aim at developing big data and new artificial intelligence technologies to contribute to the understanding of health and diseases in whole populations and whole health systems, and returning actionable feedback loops to improve existing models of research and care. The use of "real-world" data is continuously increasing but the ultimate role of this data in clinical research remains to be determined.

A Collaborative Platform for Management of Chronic Diseases via Guideline-Driven Individualized Care Plans

Older age is associated with an increased accumulation of multiple chronic conditions. The clinical management of patients suffering from multiple chronic conditions is very complex, disconnected and time-consuming with the traditional care settings. Integrated care is a means to address the growing demand for improved patient experience and health outcomes of multimorbid and long-term care patients. Care planning is a prevalent approach of integrated care, where the aim is to deliver more personalized and targeted care creating shared care plans by clearly articulating the role of each provider and patient in the care process. In this paper, we present a method and corresponding implementation of a semi-automatic care plan management tool, integrated with clinical decision support services which can seamlessly access and assess the electronic health records (EHRs) of the patient in comparison with evidence based clinical guidelines to suggest personalized recommendations for goals and interventions to be added to the individualized care plans. We also report the results of usability studies carried out in four pilot sites by patients and clinicians.