Table 1 Survey questionnaire
Socio-demographic and career profile | |
|---|---|
Gender: | Male Female Rather not say Other: |
Age in years: | Â |
Country: | Austria Belgium Bulgaria Croatia Republic of Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden Other, please specify: |
Affiliated European reference network (multiple responses allowed) | ERN BOND – European Reference Network on bone disorders ERN CRANIO – European Reference Network on craniofacial anomalies and ear, nose and throat (ENT) disorders Endo-ERN – European Reference Network on endocrine conditions ERN EpiCARE – European Reference Network on epilepsies ERKNet – European Reference Network on kidney diseases ERN-RND – European Reference Network on neurological diseases ERNICA – European Reference Network on inherited and congenital anomalies ERN LUNG – European Reference Network on respiratory diseases ERN Skin – European Reference Network on skin disorders ERN EURACAN – European Reference Network on adult cancers (solid tumours) ERN EuroBloodNet – European Reference Network on haematological diseases ERN eUROGEN – European Reference Network on urogenital diseases and conditions ERN EURO-NMD – European Reference Network on neuromuscular diseases ERN EYE – European Reference Network on eye diseases ERN GENTURIS – European Reference Network on genetic tumour risk syndromes ERN GUARD-HEART – European Reference Network on diseases of the heart ERN ITHACA – European Reference Network on congenital malformations and rare intellectual disability MetabERN – European Reference Network on hereditary metabolic disorders ERN PaedCan – European Reference Network on paediatric cancer (haemato-oncology) ERN RARE-LIVER – European Reference Network on hepatological diseases ERN ReCONNET – European Reference Network on connective tissue and musculoskeletal diseases ERN RITA – European Reference Network on immunodeficiency, autoinflammatory and autoimmune diseases ERN TRANSPLANT-CHILD – European Reference Network on Transplantation in Children VASCERN – European Reference Network on Rare Multisystemic Vascular Diseases |
Medical specialty (multiple responses allowed) | Accident and emergency medicine Allergology Anaesthetics Biological hematology Cardiology Child psychiatry Clinical biology Clinical chemistry Clinical neurophysiology Clinical radiology Dental, oral and maxillo-facial surgery Dermatology Dermato-venerology Endocrinology Gastro-enterologic surgery Gastroenterology General hematology General practice General surgery Geriatrics Immunology Infectious diseases Internal medicine Laboratory medicine Maxillo-facial surgery Microbiology Nephrology Neurology Neuro-psychiatry Neurosurgery Nuclear medicine Obstetrics and gynecology Occupational medicine Ophthalmology Orthopaedics Otorhinolaryngology Paediatric surgery Paediatrics Pathology Pharmacology Physical medicine and rehabilitation Plastic surgery Podiatric medicine Podiatric surgery Psychiatry Public health and preventive medicine Radiology Radiotherapy Respiratory medicine Rheumatology Stomatology Thoracic surgery Tropical medicine Urology Vascular surgery Venereology Other, please specify: |
Professional experience in years: | Â |
Main professional sector (> 50% of the working time): | Public Private Equally |
Main professional role: | Administration Diagnosis and treatment Research Teaching Other, please specify: |
Participant’s knowledge and attitude towards machine learning | |
How would you assess your knowledge of ML on a 1–5 scale (1 being the lowest and 5 being the highest)? |  |
Have you ever used ML in your clinical practice (for example, as a research project or as a routine technique)? | Yes, I have extensive experience Yes, I have limited experience No, I have no experience |
Based on your knowledge on and experience with ML so far, how would you assess each one of the following potential benefits of ML on a 1–5 scale (1 being the least important and 5 being the most important)? | Improved diagnostic accuracy More efficient workflows Less time spent on administrative and other mundane tasks Synthesis of clinical information Updating of clinical records More time spent with patients Improved access to care |
Would you like to comment on other potential benefits of ML that are not listed above? | Â |
Based on your knowledge on and experience with ML so far, how would you assess each one of the following potential risks of ML on a 1–5 scale (1 being the least important and 5 being the most important)? | Liability for ML-mediated errors Insufficient training and continuing professional development in ML Reputational loss and reduced demand for specialist opinion Potential erosion of empathetic communication with patients Risk of privacy breaches and loss of confidentiality of patient information Lack of proof of efficacy of ML applications in clinical settings Lack of accuracy, fairness, transparency and decision-making power of the ML outcomes |
Would you like to comment on other potential risks of ML that are not listed above? | Â |
Participant’s attitudes towards machine learning’s potential implementation and integration in healthcare | |
In case of ML being routinely applied in the diagnostic process, what do you think is the most appropriate way to mandate this process? | ML should be a mandatory part of the diagnostic process ML should be an optional, but recommended part of the diagnostic process ML should be available only upon patient’s request Other, please specify: |
In case of ML being routinely applied in the diagnostic process, what types of ML results should be disclosed to patients? | All results should be disclosed to patients Scope and type of results to be disclosed should be regulated at EU level (for example, EU regulation) Scope and type of results to be disclosed should be regulated at national level (for example, national regulation) Scope and type of results to be disclosed should be regulated by local medical associations (for example, guidelines) Physicians should choose what types of results to disclose to patients Patients should choose what types of results they would like to receive Physicians should choose what types of results to disclose to patients with an option of further decision by the patient Other, please specify: |
In case of ML being routinely applied in the diagnostic process, what do you think should be the main source to fund this activity? | Public funding (for example, government subsidy, reimbursement, etc.) Private funding (for example, direct payment by the patients) Mixed (public–private funding) Funding through research projects Funding through research projects and subsequent public funding if justified Other, please specify: |
In case of ML being routinely applied in the diagnostic process, what do you think is the most appropriate way to regulate secondary use of anonymized ML-generated diagnostic data? | Anonymized ML-generated diagnostic data should not be available for secondary use Anonymized ML-generated diagnostic data should be available for secondary use only with patients’ consent (consent required) Anonymized ML-generated diagnostic data should be available for secondary use without patients’ consent, but patients can opt out (assumed consent) Anonymized ML-generated diagnostic data should be available for secondary use without patients’ consent (no consent required) Other, please specify: |
Participant’s attitudes towards machine learning’s prospects | |
What would you expect the application of ML to be in the next 5Â years? | ML is routinely applied in all clinical settings and all levels of health care, including autonomously by patients themselves (wide application with no restrictions) ML is routinely applied only in designated centres of expertise and European reference networks (restricted application in specialized units only) ML is only applied within the framework of research projects (no change from the current situation) |
How would you assess each one of the following influencing factors, so you could promote the routine application of and access to ML outside research projects? (1 being the least important and 5 being the most important)? | Ensuring accuracy, freedom from bias, trustworthiness Improving efficiency and reducing administrative burden Improving clinical decision-making and outcomes Maintaining the integrity of clinician – patient relationships Preserving professional status Obtaining regulatory approval Determining liability for error Ensuring data privacy, confidentiality and security Ensuring access and equity |
How would you assess each one of the following criteria, so you could promote the routine application of and access to ML outside research projects? (1 being the least important and 5 being the most important)? ML diagnostic tools must be: | Based on models that have involved domain experts and have minimised bias Fitted to and complement routine clinical workflows and, where possible, self-populate the required data with minimal clinician input Shown to be as or more effective in improving clinical decision-making than current care Not distracting from, or degrading, human to human interaction and shared decision-making Developed and assessed with an eye to maximising explainability and transparency in regards to their inner workings Implemented with care regarding potential loss of jobs or professional reputation Subject to regulatory standards that are robust, transparent and responsive to updates of existing applications Associated with clear lines of responsibility regarding liability for error Adhering to legal and community expectations regarding privacy, confidentiality and security of health and medical data Equitably accessible to low income, remote or other disadvantaged populations |
If you would like to comment on the survey and/or provide additional information and suggestions, please, use this field: | Â |
Would you be willing to participate in an online focus group discussion with selected ERN stakeholders regarding the outcomes of this survey? If yes, please provide your name and contact e-mail in this field: | Â |