scholarly journals Blockchain for Healthcare: Securing Patient Data and Enabling Trusted Artificial Intelligence

2020 ◽  
Vol 6 (3) ◽  
pp. 15 ◽  
Author(s):  
H. S. Jennath ◽  
V S Anoop ◽  
S Asharaf
Keyword(s):  
Artificial Intelligence ◽  
Patient Data

scholarly journals Physicians’ requirements and expectations of future Artificial Intelligence applications for healthcare – a web-based survey in German university hospitals (Preprint)

2020 ◽  
Author(s):  
Oliver Maassen ◽  
Sebastian Fritsch ◽  
Julia Gantner ◽  
Saskia Deffge ◽  
Julian Kunze ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Patient Care ◽  
Biomedical Research ◽  
Positive Attitude ◽  
Patient Data ◽  
University Hospitals ◽  
Web Based ◽  
Wide Range ◽  
Medical Disciplines

BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of healthcare. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research has not been investigated widely in German university hospitals. OBJECTIVE Evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research e.g. for the development of machine learning (ML) algorithms in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given on Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS 121 (39.9%) female and 173 (57.1%) male physicians (N=303) from a wide range of medical disciplines and work experience levels completed the online survey. The majority of respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). 82.5% of respondents (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism, there come several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (e.g., imaging procedures in radiology and pathology) or data is collected continuously (e.g. cardiology and intensive care medicine), physicians’ expectations to substantially improve future patient care are high. However, for the practical usage of AI in healthcare regulatory and organizational challenges still have to be mastered.

scholarly journals Patient Data-Sharing for AI: Ethical Challenges, Catholic Solutions

2020 ◽  
Vol 87 (4) ◽  
pp. 471-481
Author(s):  
Jean Baric-Parker ◽  
Emily E. Anderson
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Electronic Health Record ◽  
Data Sharing ◽  
Patient Data ◽  
Health Record ◽  
Social Challenges ◽  
Ai Ethics ◽  
Guiding Principles ◽  
Electronic Health

Recent news of Catholic and secular healthcare systems sharing electronic health record (EHR) data with technology companies for the purposes of developing artificial intelligence (AI) applications has drawn attention to the ethical and social challenges of such collaborations, including threats to patient privacy and confidentiality, undermining of patient consent, and lack of corporate transparency. Although the United States Catholic Conference of Bishops’ Ethical and Religious Directives for Health Care Services ( ERDs) address collaborations between US Catholic healthcare providers and other entities, the ERDs do not adequately address the novel concerns seen in EHR data-sharing for AI development. Neither does the Health Insurance Portability and Accountability Act (HIPAA) privacy rule. This article describes ethical and social problems observed in recent patient data-sharing collaborations with AI companies and analyzes them in light of the guiding principles of the ERDs as well as the 2020 Rome Call to AI Ethics ( RCAIE) document recently released by the Vatican. While both the ERDs and RCAIE guiding principles can inform future collaborations, we suggest that the next revision of the ERDs should consider addressing data-sharing and AI more directly. Summary: Electronic health record data-sharing with artificial intelligence developers presents unique ethical and social challenges that can be addressed with updated United States Catholic Conference of Bishops’ Ethical and Religious Directives and guidance from the Vatican’s 2020 Rome Call to AI Ethics.

Deep Learning of Data Analytics in Healthcare

2020 ◽  
pp. 151-165
Author(s):  
Yang Lu
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Data Processing ◽  
Exponential Growth ◽  
Data Analytics ◽  
Disease Diagnosis ◽  
Patient Data ◽  
Healthcare Industry ◽  
Processing Capability ◽  
Data Volume

The importance of data as the fuel of artificial intelligence is self-evident. As the degree of informatization in various industries deepens, the amount of accumulated data continues to increase; however, data processing capability lags far behind the exponential growth of data volume. To gather accurate results, more and more data should be collected. However, the more data collected, the slower the processing and analyzing of that data. The emergence of deep learning solves the problem of how to process large amounts of data quickly and precisely. With the advancement of technology, the healthcare industry has achieved a promising level of needed data. Moreover, if deep learning can be used to aid disease diagnosis, patient data can be processed efficiently, useful information can be screened, valuable diagnostic rules can be mined, and disease diagnosis results can be better formulated and treated. It is foreseeable that deep learning has the potential to improve the effectiveness and the efficiency of healthcare and relevant industries.

scholarly journals Design of an E-Health platform using artificial intelligence technologies for the surveillance and prevention of patients with cardiovascular diseases against covid19

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
N Mouine ◽  
A Hilmani ◽  
A Maizate ◽  
C Mahmoudi ◽  
A Benyass
Keyword(s):  
Artificial Intelligence ◽  
Heart Rate ◽  
Vascular Disease ◽  
Arterial Oxygen Saturation ◽  
Patient Data ◽  
Arterial Oxygen ◽  
Central System ◽  
Health Indices ◽  
Cardio Vascular Disease ◽  
Cardio Vascular

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Covid-19 disease is caused by SARS-CoV-2.The symptomatology is variable, it can range from a common cold to a severe acute respiratory distress. Severe forms are mainly seen in patients with cardio vascular disease, they are at very high risk of mortality. The aim of our project is to design and producean E-Health platform to enable telemedicine acts such as telemonitoring and assistance of patients with cardio vascular disease to prevent covid 19 infection Materials and methods It is an e-health platform that uses digital technologies associated with artificial intelligence to provide remote monitoring and assistance to patients; It consists of two parts: the acquisition of patient data, a gateway and a central system. Acquisition of patient data by sensors equipped with a wireless data transmission device allowing the recovery of patient health indices such as heart rate, respiratory rate ..., a mobile application which allows to acquire data emitted by the sensors placed on the patient, which includes an AI module that analyzes the data in real time in order to send alerts to the patient Expected results Through telemedicine, patients with cardiac diseases will be under continuous monitoring of hemodynamic parameters: Temperature, Arterial oxygen saturation, Heart Rate, Blood Pressure, Electrocardiogram ...,,these data will be processed by an AI module which will analyze the results and will detect anomalies. The latter will give recommendations and immediately alert the patient Conclusion Telemedicine is a new and innovative concept, it will improve the health care and will have a great socio-economic impact on bothpatient andhealth services. it"s can help to fight against Covid 19 infection.

scholarly journals Analyzing Patient Trajectories With Artificial Intelligence (Preprint)

2021 ◽  
Author(s):  
Ahmed Allam ◽  
Stefan Feuerriegel ◽  
Michael Rebhan ◽  
Michael Krauthammer
Keyword(s):  
Artificial Intelligence ◽  
Patient Data ◽  
Problem Definition ◽  
Future Directions ◽  
Health Events ◽  
Additional Information ◽  
Digital Medicine ◽  
Effective Care ◽  
Risk Scoring ◽  
Pathway Discovery

UNSTRUCTURED In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery.

scholarly journals Analyzing Patient Trajectories With Artificial Intelligence

10.2196/29812 ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. e29812
Author(s):  
Ahmed Allam ◽  
Stefan Feuerriegel ◽  
Michael Rebhan ◽  
Michael Krauthammer
Keyword(s):  
Artificial Intelligence ◽  
Patient Data ◽  
Problem Definition ◽  
Future Directions ◽  
Health Events ◽  
Additional Information ◽  
Digital Medicine ◽  
Effective Care ◽  
Risk Scoring ◽  
Pathway Discovery

In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery.

scholarly journals Predicting Cardiac Arrest and Respiratory Failure Using Feasible Artificial Intelligence with Simple Trajectories of Patient Data

2019 ◽  
Vol 8 (9) ◽  
pp. 1336 ◽  
Author(s):  
Jeongmin Kim ◽  
Myunghun Chae ◽  
Hyuk-Jae Chang ◽  
Young-Ah Kim ◽  
Eunjeong Park
Keyword(s):  
Artificial Intelligence ◽  
Cardiac Arrest ◽  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Early Warning ◽  
Vital Signs ◽  
Patient Data ◽  
Early Warning Score ◽  
Net Reclassification Improvement ◽  
Fast Pace

We introduce a Feasible Artificial Intelligence with Simple Trajectories for Predicting Adverse Catastrophic Events (FAST-PACE) solution for preparing immediate intervention in emergency situations. FAST-PACE utilizes a concise set of collected features to construct an artificial intelligence model that predicts the onset of cardiac arrest or acute respiratory failure from 1 h to 6 h prior to its occurrence. Data from the trajectory of 29,181 patients in intensive care units of two hospitals includes periodic vital signs, a history of treatment, current health status, and recent surgery. It excludes the results of laboratory data to construct a feasible application in wards, out-hospital emergency care, emergency transport, or other clinical situations where instant medical decisions are required with restricted patient data. These results are superior to previous warning scores including the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS). The primary outcome was the feasibility of an artificial intelligence (AI) model predicting adverse events 1 h to 6 h prior to occurrence without lab data; the area under the receiver operating characteristic curve of this model was 0.886 for cardiac arrest and 0.869 for respiratory failure 6 h before occurrence. The secondary outcome was the superior prediction performance to MEWS (net reclassification improvement of 0.507 for predicting cardiac arrest and 0.341 for predicting respiratory failure) and NEWS (net reclassification improvement of 0.412 for predicting cardiac arrest and 0.215 for predicting respiratory failure) 6 h before occurrence. This study suggests that AI consisting of simple vital signs and a brief interview could predict a cardiac arrest or acute respiratory failure 6 h earlier.

scholarly journals Privacy and artificial intelligence: challenges for protecting health information in a new era

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Blake Murdoch
Keyword(s):  
Artificial Intelligence ◽  
Health Information ◽  
Patient Data ◽  
Main Body ◽  
New Era ◽  
Data Anonymization ◽  
Patient Agency ◽  
Patient Health ◽  
External Risk ◽  
Privacy Breaches

Abstract Background Advances in healthcare artificial intelligence (AI) are occurring rapidly and there is a growing discussion about managing its development. Many AI technologies end up owned and controlled by private entities. The nature of the implementation of AI could mean such corporations, clinics and public bodies will have a greater than typical role in obtaining, utilizing and protecting patient health information. This raises privacy issues relating to implementation and data security. Main body The first set of concerns includes access, use and control of patient data in private hands. Some recent public–private partnerships for implementing AI have resulted in poor protection of privacy. As such, there have been calls for greater systemic oversight of big data health research. Appropriate safeguards must be in place to maintain privacy and patient agency. Private custodians of data can be impacted by competing goals and should be structurally encouraged to ensure data protection and to deter alternative use thereof. Another set of concerns relates to the external risk of privacy breaches through AI-driven methods. The ability to deidentify or anonymize patient health data may be compromised or even nullified in light of new algorithms that have successfully reidentified such data. This could increase the risk to patient data under private custodianship. Conclusions We are currently in a familiar situation in which regulation and oversight risk falling behind the technologies they govern. Regulation should emphasize patient agency and consent, and should encourage increasingly sophisticated methods of data anonymization and protection.

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