تحلیل و رتبهبندی موانع پذیرش هوش مصنوعی در صنعت بهداشت و درمان ایران: رویکردی مبتنی بر مدلسازی ساختاری تفسیری
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمندسولماز هاشمی 1 , آرش زارع طلب 2 *
1 - گروه مدیریت بازرگانی، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران
2 - گروه مدیریت بازرگانی، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه: هوش مصنوعی, صنعت بهداشت و درمان, مدلسازی ساختاری تفسیری,
چکیده مقاله :
هدف اصلی پژوهش حاضر شناسایی رتبهبندی موانع پذیرش هوش مصنوعی در صنعت بهداشت و درمان کشور است. اين پژوهش، تحقیقی توصیفی - پیمایشی و از نظر هدف از نوع پژوهشهای کاربردی محسوب میشود. جامعه آماری این تحقیق شامل مدیران ارشد شاغل در بیمارستانهای دولتی استان تهران می باشد. دراین پژوهش برای گردآوری دادهها از دو روش کتابخانهای و میدانی استفاده شد و ابزار گردآوری دادهها نیز پرسشنامه مقایسات زوجی بوده است که در اختیار خبرگان 20 نفر از مدیران ارشد صنعت بهداشت و درمان قرار گرفت. جهت تجزیه و تحلیل دادهها از روش مدل سازی ساختاری تفسیری استفاده شد. پس از استخراج موانع پذیرش هوش مصنوعی در صنعت بهداشت و درمان از طریق مطالعه و بررسی پیشینه و ادبیات تحقیق، با استفاده از روش تکنیک دلفی موانع پذیرش هوش مصنوعی در صنعت بهداشت و درمان با استفاده از مصاحبه با 10 نفر از خبرگان استخراج شد و به عنوان موانع پذیرش هوش مصنوعی در صنعت بهداشت و درمان انتخاب شدند. براي انجام اين پژوهش از روش مدلسازي ساختاري تفسيری استفاده شده است. ايده اصلي مدلسازي ساختاري تفسيري تجزيه يک سيستم پيچيده به چند زير سيستم با استفاده از تجربه عملي و دانش خبرگان به منظور ساخت يک مدل ساختاري چند سطحي مي باشد. در این مطالعه پس از انجام مدل ساختاری – تفسیری تحلیل میک مک انجام خواهد گرفت. هدف از تحليل میک مک تجزيه و تحليل قدرت محرکه و قدرت وابستگي متغيرها است. نتایج روش دلفی نشان داد که عوامل اخلاقی و اجتماعی و سیاستهای دولت در سطوح بنیادی و پایین مدل ساختاری تفسیری قرار دارند که نشان دهنده اهمیت این عوامل در پذیرش هوش مصنوعی در صنعت بهداشت و درمان کشور هستند.
Introduction: Recent decades have seen a tremendous growth in the capabilities and applications of artificial intelligence. Healthcare is one of the many industries that will apparently experience significant changes in the coming years. Healthcare practitioners and stakeholders in both the private and public sectors are striving to use emerging technologies to enhance the patient experience, reduce costs, and improve patient outcomes. IoT, cloud computing, wearables, and artificial intelligence are examples of areas of innovation that have the potential to meet the needs of the healthcare industry. The successful implementation of emerging technologies such as artificial intelligence will change the healthcare service model from traditional approaches to a value-based care approach, where personal care of patients is prioritized. The healthcare industry is a complex industry with various stakeholders such as patients, doctors, hospital administrators, suppliers, regulatory authorities and pharmaceutical companies. Emerging technologies such as artificial intelligence, Internet of Things, and cloud computing are important to enhance healthcare services and overcome challenges in the field of medicine. Among these, artificial intelligence is expected to play an important role in addressing many challenges in healthcare, including patient access to medical services and increasing workloads in non-value-added areas that reduce their ability to serve their patients.
Method: The main goal of the current research is to identify the ranking of obstacles to the adoption of artificial intelligence in the healthcare industry of the country. This research is descriptive-survey research and is considered as applied research in terms of its purpose. The statistical population of this research includes senior managers working in public hospitals in Tehran province. In this research, two library and field methods were used for data collection, and the data collection tool was a pairwise comparison questionnaire, which was given to 20 experts from the senior managers of the healthcare industry. In order to analyze the data, the interpretive structural modeling method was used. After extracting the obstacles to the adoption of artificial intelligence in the healthcare industry through studying and reviewing the background and research literature, using the Delphi technique, the obstacles to the adoption of artificial intelligence in the healthcare industry were extracted by interviewing 10 experts and They were chosen as barriers to the adoption of artificial intelligence in the healthcare industry.
Results: The results of the Delphi method showed that the moral and social factors and government policies are at the basic and low levels of the interpretive structural model, which indicate the importance of these factors in the adoption of artificial intelligence in the country's healthcare industry.
Discussion: Overall, the results of this study suggest that healthcare organizations should improve data collection systems and create shared data platforms for health data. Developers should focus on creating AI systems that provide output in a language that human users can understand. This can facilitate the psychological acceptance of the technology. Healthcare organizations should also implement change management programs, ongoing training, and incentive systems to encourage employees to use new technologies. The practical implications of these results can help policymakers, hospital administrators, and AI technology developers more effectively overcome these barriers.
Artificial intelligence has the potential to overcome staff shortages in developing and developed countries, increase organizational efficiency, and improve diagnostic accuracy as well as patient outcomes by providing minimal results. Comparable in terms of quality compared to humans, to maximize. As a result, AI may reduce costs by avoiding inefficiencies, unnecessary treatments and late diagnoses. In general, it can be said that artificial intelligence in health care can support doctors, automatically analyze clinical documents and medical images, and also help in virtual observation, diagnosis and informing the patient.
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