Artificial intelligence-assisted decision-making in long-term care: a qualitative study on opportunities and prerequisites for responsible innovation (Preprint)

Background: While use of artificial intelligence (AI)-based technologies such as decision-support systems (AI-DSSs) could help sustaining and improving the quality and efficiency of care, their deployment also creates ethical and social challenges. In recent years, there has been a growing prevalence of high-level guidelines and frameworks to provide guidance on responsible AI innovation. However, few studies specify how AI-based technologies such as AI-DSSs can be responsibly embedded in specific contexts such as the nursing process in the long-term care (LTC) for older adults. Objective: Opportunities and prerequisites for responsible AI-assisted decision-making in the nursing process were explored from the perspectives of nurses and other professional stakeholders in LTC. Methods: Semi-structured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists and care centralists. Two imaginary scenarios about the future use of AI-DSSs were developed beforehand and used to enable participants to articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. After first openly discussing opportunities and possible risks associated with both scenarios, six high-level principles for responsible AI were used as probing themes to evoke further consideration on risks of using AI-DSSs in LTC. Further, participants were asked to brainstorm about possible strategies and actions in the design, implementation and use of AI-DSSs to address or mitigate the mentioned risks. A thematic analysis was carried out to identify opportunities and prerequisites for responsible innovation in this area. Results: Professionals’ stance towards the use of AI-DSSs is not a matter of purely positive or negative expectations, but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of opportunities and prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to early identification of care needs, guidance in devising care strategies, shared decision-making, and caregivers’ workload and work experience. To optimally balance opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AI-assisted decision-making in the nursing process were identified: (1) regular deliberation on data collection, (2) a balanced proactive nature of AI-DSSs, (3) incremental advancements aligned with trust and experience, (4) customization for all user groups including clients and caregivers, (5) measures to counteract bias and narrow perspectives, (6) human-centric learning loops, and (7) routinization of using AI-DSSs. Conclusions: Opportunities of AI-assisted decision-making in the nursing process could turn into drawbacks, depending on the specific shaping of the design and the deployment of AI-DSSs. Therefore, we recommend viewing the responsible use of AI-DSSs as a balancing act. Moreover, given the interrelatedness of the identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address different factors important to the responsible embedding of AI-DSSs in practice

Case Reports1. A Late Presentation of Loeys-Dietz Syndrome: Beware of TGFβ Receptor Mutations in Benign Joint Hypermobility

Background: Thoracic aortic aneurysms (TAA) and dissections are not uncommon causes of sudden death in young adults. Loeys-Dietz syndrome (LDS) is a rare, recently described, autosomal dominant, connective tissue disease characterized by aggressive arterial aneurysms, resulting from mutations in the transforming growth factor beta (TGFβ) receptor genes TGFBR1 and TGFBR2. Mean age at death is 26.1 years, most often due to aortic dissection. We report an unusually late presentation of LDS, diagnosed following elective surgery in a female with a long history of joint hypermobility. Methods: A 51-year-old Caucasian lady complained of chest pain and headache following a dural leak from spinal anaesthesia for an elective ankle arthroscopy. CT scan and echocardiography demonstrated a dilated aortic root and significant aortic regurgitation. MRA demonstrated aortic tortuosity, an infrarenal aortic aneurysm and aneurysms in the left renal and right internal mammary arteries. She underwent aortic root repair and aortic valve replacement. She had a background of long-standing joint pains secondary to hypermobility, easy bruising, unusual fracture susceptibility and mild bronchiectasis. She had one healthy child age 32, after which she suffered a uterine prolapse. Examination revealed mild Marfanoid features. Uvula, skin and ophthalmological examination was normal. Results: Fibrillin-1 testing for Marfan syndrome (MFS) was negative. Detection of a c.1270G > C (p.Gly424Arg) TGFBR2 mutation confirmed the diagnosis of LDS. Losartan was started for vascular protection. Conclusions: LDS is a severe inherited vasculopathy that usually presents in childhood. It is characterized by aortic root dilatation and ascending aneurysms. There is a higher risk of aortic dissection compared with MFS. Clinical features overlap with MFS and Ehlers Danlos syndrome Type IV, but differentiating dysmorphogenic features include ocular hypertelorism, bifid uvula and cleft palate. Echocardiography and MRA or CT scanning from head to pelvis is recommended to establish the extent of vascular involvement. Management involves early surgical intervention, including early valve-sparing aortic root replacement, genetic counselling and close monitoring in pregnancy. Despite being caused by loss of function mutations in either TGFβ receptor, paradoxical activation of TGFβ signalling is seen, suggesting that TGFβ antagonism may confer disease modifying effects similar to those observed in MFS. TGFβ antagonism can be achieved with angiotensin antagonists, such as Losartan, which is able to delay aortic aneurysm development in preclinical models and in patients with MFS. Our case emphasizes the importance of timely recognition of vasculopathy syndromes in patients with hypermobility and the need for early surgical intervention. It also highlights their heterogeneity and the potential for late presentation. Disclosures: The authors have declared no conflicts of interes

Speech Technology in the Dutch Health Care: A Qualitative Study

This study investigates the opportunities of speech technology in Dutch hospitals, and to what extent speech technology can be used for documentation. Furthermore, we clarify why speech technology is used only marginally by Dutch hospital staff. We performed interviews where speech technology users, managers in hospitals and software suppliers were contacted as participants. We then transcribed our interviews and synthesized the pros and cons of speech technology as well as major barriers for the adoption. Our results show various influencing factors that could be clarifications for the fact that only 1% of the medical staff uses speech technology in the Netherlands. The major reasons we found are: speech technology usage at only radiology and pathology departments, \emph{smarttexts} and \emph{smartphrases} of the Electronic Health Record (EHR) compete with speech technology, caregivers have to adjust their way of working which evokes resistance, lack of central authorization at Dutc h hospitals and finally, financial barriers. Our results show that speech technology works for radiology and pathology as a tool for documentation, but is found less useful for other departments. For the remaining departments, different applications show potential, such as structured reporting

Speech Technology in the Dutch Health Care: A Qualitative Study

This study investigates the opportunities of speech technology in Dutch hospitals, and to what extent speech technology can be used for documentation. Furthermore, we clarify why speech technology is used only marginally by Dutch hospital staff. We performed interviews where speech technology users, managers in hospitals and software suppliers were contacted as participants. We then transcribed our interviews and synthesized the pros and cons of speech technology as well as major barriers for the adoption. Our results show various influencing factors that could be clarifications for the fact that only 1% of the medical staff uses speech technology in the Netherlands. The major reasons we found are: speech technology usage at only radiology and pathology departments, \emph{smarttexts} and \emph{smartphrases} of the Electronic Health Record (EHR) compete with speech technology, caregivers have to adjust their way of working which evokes resistance, lack of central authorization at Dutc h hospitals and finally, financial barriers. Our results show that speech technology works for radiology and pathology as a tool for documentation, but is found less useful for other departments. For the remaining departments, different applications show potential, such as structured reporting

Making co-design more responsible: a case study on developing an AI-based decision support system in dementia care (Preprint)

Background: Emerging technologies like artificial intelligence (AI) require early-stage assessment of potential societal and ethical implications to increase their acceptability, desirability and sustainability. This paper explores and compares two of these assessment approaches: the responsible innovation framework originating from technology studies and the co-design approach coming from design studies. While the responsible innovation (RI) framework has been introduced to guide early-stage technology assessment through anticipation, inclusion, reflexivity and responsiveness, co-design is a commonly accepted approach in the development of technologies to support the care for frail older adults. However, there is limited understanding about how co-design contributes to anticipation of implications. Objective: This paper empirically explores how the co-design process of an AI-based decision support system (DSS) for dementia caregivers is complemented by explicit anticipation of implications. Methods: The case investigated is a international collaborative project that focused on the co-design, development, testing and commercialization of a DSS that is intended to provide actionable information to formal caregivers of people with dementia. In parallel to the co-design process, an RI exploration took place, which involved examining project members’ viewpoints on both positive and negative implications of using the DSS, along with strategies to address these implications. Results from the co-design process and RI exploration were analyzed and compared. In addition, retrospective interviews were held with project members to reflect on the co-design process and the RI exploration. Results: Our results indicate that, when involved in exploring requirements for the DSS, co-design participants naturally raised various implications and conditions for responsible design and deployment: protecting privacy, preventing cognitive overload, providing transparency, empowering caregivers to be in control, safeguarding accuracy and training users. Yet, when comparing the co-design results with insights from the RI exploration, we also found limitations to the co-design results, for instance regarding the specification, interrelatedness and context-dependency of implications and strategies to address implications. Conclusions: This case study shows that a co-design process that focuses on opportunities for innovation rather than balancing attention for both positive and negative implications, may result in knowledge gaps related to social and ethical implications and how these can be addressed. In the pursuit of responsible outcomes, co-design facilitators could broaden their scope and reconsider the specific implementation of the process-oriented RI principles of anticipation and inclusion