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 Gestational diabetes prevention and treatment: a protocol for developing core outcome sets

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e030574 ◽  
Author(s):  
Aoife Maria Egan ◽  
Fidelma P Dunne ◽  
Linda M Biesty ◽  
Delia Bogdanet ◽  
Caroline Crowther ◽  
...  
Keyword(s):  
Gestational Diabetes ◽  
Selective Reporting ◽  
Core Outcome ◽  
University Hospitals ◽  
Web Based ◽  
Core Outcome Sets ◽  
Wide Range ◽  
Prevention And Treatment ◽  
Ethical Approval ◽  
Research Findings

IntroductionSelective reporting bias, inconsistency in the chosen outcomes between trials and irrelevance of the chosen outcomes for women, limit the efficiency and value of research for prevention and treatment of gestational diabetes mellitus (GDM). One way to address these challenges is to develop core outcome sets (COSs).Methods and analysisThe aim of this manuscript is to present a protocol for a study to develop COSs for GDM prevention and treatment. This is a three-phase project consisting of (1) a systematic review of the literature to create two lists of outcomes that have been reported in trials and systematic reviews of trials of interventions for the prevention and treatment of GDM, (2) a three-round, web-based e-Delphi survey with key stakeholders to prioritise these outcomes and (3) a consensus meeting to resolve any remaining disagreements and to agree on two COSs.Ethics and disseminationEthical approval to conduct this study was obtained from the ethics committee at Galway University Hospitals on 13 December 2018 (Reference: C.A.2078). We will disseminate our research findings through peer-reviewed, open access publications and present at international conferences to reach a wide range of knowledge users.

scholarly journals PERANCANGAN SISTEM INFORMASI REKAM MEDIS PASIEN RAWAT JALAN BERBASIS WEB DI KLINIK SEHAT MARGASARI BANDUNG

2017 ◽  
Vol 3 (3) ◽  
Author(s):  
Johni S Pasaribu ◽  
Johnson Sihombing
Keyword(s):  
Information System ◽  
Patient Care ◽  
Medical Record ◽  
Medical Records ◽  
Fundamental Problem ◽  
Patient Data ◽  
Health Clinic ◽  
Web Based ◽  
Expected Outcome ◽  
Care Information

[Id]Sistem infomasi rekam medis pasien rawat jalan adalah sistem informasi yang bertujuan mengelola data pasien yang berobat hingga pasien tersebut keluar dari rumah sakit atau klinik pada periode tertentu. Sistem informasi yang dirancang sangatlah penting untuk mencegah terjadinya kesalahan prosedur dalam pelaksanaan pendaftaran dan pengelolaan data. Sistem informasi dalam klinik kesehatan ini adalah sistem informasi yang berisikan data pasien, data obat, data transaksi dan rekam medis pasien. Adapun sebelumnya kinerja sistem dalam pelayanan pasien yang berjalan pada klinik kesehatan secara umum belum optimal karena masih pada pengolahan data pasien dan data rekam medis masih menggunakan media pembukuan atau manual. Pengelolaan data pasien di Klinik Sehat Margasari masih belum efektif karena sistem yang digunakan kurang lengkap sehingga pelayanan pasien menjadi lambat dan rekam pasien sering hilang atau tidak ditemukan. Maka pelayanan pasien menjadi tidak efektif dan efisien, karena sistem manual pembukuan memperlambat pembuatan laporan atau pencarian data pasien. Sistem informasi pelayanan pasien dirancang bertujuan untuk membangun sistem informasi yang terkomputerisasi, sehingga memudahkan pihak klinik kesehatan mengolah data pasien, obat, transaksi, rekam medis, tindakan medis pasien hingga pencetakan laporan.Hasil yang diharapkan dari penelitian ini yaitu terbangunnya sistem informasi rekam medis berbasis web untuk memudahkan Klinik Sehat Margasari dalam membantu pengolahan data pasien, obat, transaksi, rekam medis, tindakan medis pasien hingga pencetakan laporan. Rumusan masalah dari penelitian ini adalah bagaimana membangun sistem informasi rekam medis di Klinik Sehat Margasari sehingga dapat menyajikan informasi yang akurat serta efisien. Adapun tujuan dari penelitian ini adalah menghasilkan suatu sistem informasi rekam medis pasien rawat jalan.Kata kunci : Sistem Informasi, Rekam Medis, Pasien Rawat Jalan[En]Medical record outpatient information system is a system that aims to manage the data of patients who register for treatment until the patient is discharged from the hospital or health center in a given period. The information system is important because it is designed to prevent errors in the execution of the procedure of registration and data management so that it can be done as well as possible. This information systems in health clinic is an information system that has patient data, drug data, transaction data and medical records of the patient. As before for the performance of the system in patient care in health clinic in generally not optimal because it is still in the processing of patient data and medical records are still using books or manuals. Management of patient data at the Health Clinic Margasari Bandung is still not effective because the system used is less complete so that the patient's service to be slow and patient records are often missing or was not found. Therefore care patients at health clinic become ineffective and inefficient, because manual system making slow reporting or searching data patient. Patient care information system designed aiming to establish a computerized information system, making it easier for the health clinic process patient data, drugs, transaction, medical records, medical actions to patient until print out of reports. The expected outcome of this research is to build information system web-based to facilitate Health Clinic Margasari Bandung making it easier for the health clinic process patient data, drugs, transaction, medical records, medical actions to patient until print out of reports. Fundamental problem of this research is how to install information system for medical record patient information system at Health Clinic Margasari that make information representation accurately and efficiently. The aim of this research is to produce a information system of medical record outpatient.

scholarly journals Artificial intelligence in human genomics and biomedicine

2021 ◽  
Vol 30 (3) ◽  
pp. 30-36
Author(s):  
Reinhard Heil ◽  
Nils B. Heyen ◽  
Martina Baumann ◽  
Bärbel Hüsing ◽  
Daniel Bachlechner ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Practice ◽  
Individual Differences ◽  
Biomedical Research ◽  
Innovation System ◽  
Complex Data ◽  
Causal Knowledge ◽  
Human Genomics ◽  
Key Issues

The increasing availability of extensive and complex data has made human genomics and its applications in (bio)medicine an at­ tractive domain for artificial intelligence (AI) in the form of advanced machine learning (ML) methods. These methods are linked not only to the hope of improving diagnosis and drug development. Rather, they may also advance key issues in biomedicine, e. g. understanding how individual differences in the human genome may cause specific traits or diseases. We analyze the increasing convergence of AI and genom­ics, the emergence of a corresponding innovation system, and how these associative AI methods relate to the need for causal knowledge in biomedical research and development (R&D) and in medical prac­tice. Finally, we look at the opportunities and challenges for clinical practice and the implications for governance issues arising from this convergence.

scholarly journals An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude

2021 ◽  
Author(s):  
Merel Huisman ◽  
Erik Ranschaert ◽  
William Parker ◽  
Domenico Mastrodicasa ◽  
Martin Koci ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Positive Attitude ◽  
Specific Knowledge ◽  
Radiology Training ◽  
Academic Center ◽  
Knowledge Levels ◽  
Radiology Residents ◽  
Knowledge And Attitude ◽  
Clinical Adoption

Abstract Objectives Radiologists’ perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond. Methods Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression. Results The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24–74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10–2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20–0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21–0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25–31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16–50.54, p < 0.001). Conclusions Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption. Key Points • Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption.

scholarly journals On evaluation metrics for medical applications of artificial intelligence

2021 ◽  
Author(s):  
Steven Hicks ◽  
Inga Strüke ◽  
Vajira Thambawita ◽  
Malek Hammou ◽  
Pål Halvorsen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Patient Care ◽  
Open Source ◽  
Performance Metrics ◽  
Improve Patient Care ◽  
Evaluation Metrics ◽  
Medical Applications ◽  
Web Based ◽  
Improve Patient

Clinicians and model developers need to understand how proposed machine learning (ML) models could improve patient care. In fact, no single metric captures all the desirable properties of a model and several metrics are typically reported to summarize a model's performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research.

Miles, A. & Mezzich, J.E. (2011). The care of the patient and the soul of the clinic: person-centered medicine as an emergent model of modern clinical practice. International Journal of Person Centered Medicine 1 (2) 207-222

2014 ◽  
Vol 2 (1) ◽  
pp. 54
Author(s):  
Lillian Geza Rothenberger
Keyword(s):  
Clinical Practice ◽  
Patient Care ◽  
Evidence Base ◽  
Scientific Data ◽  
Research Progress ◽  
Inclusion Criteria ◽  
Study Results ◽  
The Individual ◽  
Medical Disciplines ◽  
Person Centered Approach

In recent years, there has undoubtedly been an “over-reliance on science” that has led to an unedifying “scientistic medicine” in patient care of most if not all medical disciplines [1]. Although I would not go so far as to say that these circumstances may lead to “an ethical and moral chaos within clinical practice,” I agree with Miles and Mezzich that “while it is imperative that medicine must be actively and continuously informed by science, science cannot function as the base of medicine.” Evidence generated by clinical or, in the early beginnings of research progress, even non-clinical trials can be very helpful, but to impose study results uncritically upon the individual patient would mean to neglect this person’s individuality. The individual needs may, for example, not be consistent with the inclusion criteria of the very study used as the evidence base for the treatment. I therefore welcome, then, the article by Miles and Mezzich, which discusses the need for a more critical reflection on scientific data before or within clinical use and the demand for more humanity in patient care in terms of a person-centered approach.

scholarly journals Mobile Artificial Intelligence Applications for Skin Cancer Diagnostics: Preferences and Concerns of Digital Natives (Preprint)

2020 ◽  
Author(s):  
Sarah Haggenmüller ◽  
Eva Krieghoff-Henning ◽  
Tanja Jutzi ◽  
Nicole Trapp ◽  
Lennard Kiehl ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Mobile Applications ◽  
Positive Attitude ◽  
Age Groups ◽  
Digital Natives ◽  
Cancer Diagnostics ◽  
Skin Cancer Detection

BACKGROUND Artificial Intelligence (AI) has shown potential to improve diagnostics of various diseases and especially early skin cancer detection. What is missing is the bridge from AI technology to clear application scenarios in clinical practice as well as added value for patients. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation into clinical practice, while at the same time representing the future generation of skin cancer screening participants. OBJECTIVE We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile applications for skin cancer diagnostics. In this way, we evaluated the preferences as well as the relative influence of concerns with a special focus on younger age groups. METHODS We recruited respondents below 35 years of age through the social media channels Facebook, LinkedIn and Xing. Descriptive analysis and statistical tests were performed to evaluate participants’ attitudes towards mobile applications for skin examination. An adaptive choice-based conjoint (ACBC) was integrated to assess respondents’ preferences. Potential concerns were evaluated using maximum difference scaling (MaxDiff). RESULTS 728 respondents were included in the analysis. About 66.5% expressed a positive attitude towards the use of AI-based applications. In particular participants residing in big cities or small towns and individuals that were familiar with the use of health or tracking apps were significantly more open towards mobile diagnostic systems. Hierarchical Bayes estimation (HB) of the preferences of participants with positive attitude (n=484) revealed that the use of mobile applications as an assistance system was preferred. Respondents ruled out app versions with an accuracy of 65 percent or less, applications using data storage without encryption as well as systems that did not deliver background information about the decision-making. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information into the decision-making process. MaxDiff analysis for the negative-minded participant group (n=244) outlined that data security, insufficient trust in the app, as well as the lack of personal interaction represented the dominant concerns with respect to app use. CONCLUSIONS The majority of potential future users below 35 years of age was ready to accept AI-based diagnostic solutions for early skin cancer detection. However, for translation into clinical practice, participants’ demand for increased transparency and explainability of AI-based tools seems to be critical. Altogether, digital natives expressed similar preferences and concerns when compared to results obtained by previous studies that included other age groups.

Investigating the Efficacy and Cost-Effectiveness of a Web-based Self-help Program for People With Adjustment Problems After an Accident (SelFIT): Protocol for a Randomized Controlled Trial (Preprint)

2020 ◽  
Author(s):  
Julia Hegy ◽  
Noemi Anja Brog ◽  
Thomas Berger ◽  
Hansjoerg Znoj
Keyword(s):  
Health Care ◽  
Psychological Distress ◽  
Cost Effectiveness ◽  
Well Being ◽  
User Friendliness ◽  
Adjustment Problems ◽  
Health Care Consumption ◽  
Web Based ◽  
Self Help ◽  
Wide Range

BACKGROUND Accidents and the resulting injuries are one of the world’s biggest health care issues often causing long-term effects on psychological and physical health. With regard to psychological consequences, accidents can cause a wide range of burdens including adjustment problems. Although adjustment problems are among the most frequent mental health problems, there are few specific interventions available. The newly developed program SelFIT aims to remedy this situation by offering a low-threshold web-based self-help intervention for psychological distress after an accident. OBJECTIVE The overall aim is to evaluate the efficacy and cost-effectiveness of the SelFIT program plus care as usual (CAU) compared to only care as usual. Furthermore, the program’s user friendliness, acceptance and adherence are assessed. We expect that the use of SelFIT is associated with a greater reduction in psychological distress, greater improvement in mental and physical well-being, and greater cost-effectiveness compared to CAU. METHODS Adults (n=240) showing adjustment problems due to an accident they experienced between 2 weeks and 2 years before entering the study will be randomized. Participants in the intervention group receive direct access to SelFIT. The control group receives access to the program after 12 weeks. There are 6 measurement points for both groups (baseline as well as after 4, 8, 12, 24 and 36 weeks). The main outcome is a reduction in anxiety, depression and stress symptoms that indicate adjustment problems. Secondary outcomes include well-being, optimism, embitterment, self-esteem, self-efficacy, emotion regulation, pain, costs of health care consumption and productivity loss as well as the program’s adherence, acceptance and user-friendliness. RESULTS Recruitment started in December 2019 and is ongoing. CONCLUSIONS To the best of our knowledge, this is the first study examining a web-based self-help program designed to treat adjustment problems resulting from an accident. If effective, the program could complement the still limited offer of secondary and tertiary psychological prevention after an accident. CLINICALTRIAL ClinicalTrials.gov NCT03785912; https://clinicaltrials.gov/ct2/show/NCT03785912?cond=NCT03785912&draw=2&rank=1

Sign in / Sign up

Export Citation Format

Share Document