The process of discharging patients from hospital is complex and multi-faceted, resulting in frequent delays, reduced patient satisfaction, and potentially worse patient outcomes. Challenges to the patient discharge system include those outside of the hospital’s control, such as patients waiting for a place in a care home, but also include those within the hospital, such as administrative delays or awaiting test results (1),(2). Further challenges include patient readmission, which can occur due to inadequate discharge planning (3). AI is revolutionizing many areas of healthcare, including the discharge process (4). This blog discusses current problems with the patient discharge process and how the use of AI can solve many of these issues.
How AI Overcomes Challenges in Patient Discharge
Delays and Inefficiencies
Patient discharge processes are complicated and administration-heavy. Inefficient internal hospital processes are responsible for up to 22% of discharge delays (5), providing a clear requirement for improvement. AI is able to streamline many of the administrative tasks involved in patient discharge, including the writing of discharge summaries. AI carries out these tasks faster and may even improve the quality of the paperwork (6). AI can also be used to predict which patients are likely to be discharged within the next 12 or 24 hours (7). This allows the administrative aspects of the discharge process to be initiated in advance, thus speeding up the process.
Risk of Readmission
Discharge from hospital must be carried out effectively in order to reduce the risk of readmission – this includes preparing the patient well for discharge. AI can contribute firstly by predicting those most at risk of readmission (8), allowing medical professionals to focus on those patients and divert resources so as to reduce their risk of readmission (9). In addition, AI can assist in patient education and discharge instructions. A patient is educated on their condition as part of the discharge process, including important information on new medication. A lack of understanding can result in a lack of patient adherence to instructions and medication regimes, potentially resulting in hospital readmission (10). AI can tailor information given on discharge to the health literacy level of the patient, resulting in discharge summaries that the patient can fully understand, and a reduction in readmission to hospital (11).
Benefits of AI-Assisted Patient Discharge
AI can carry out administrative tasks faster and, in many cases, more accurately than humans (12),(13). The use of AI can therefore remove some of the administrative burden from healthcare professionals, allowing them to spend more time with patients. AI can also reduce both the amount of time that patients spend in hospital and their risk of readmission, leading to improved patient satisfaction. The use of AI in patient education gives patients greater confidence in managing their own health issues (11). Furthermore, AI reduces the costs of hospital stays both by reducing the time spent in hospital (14) and reducing the risk of readmission (which can be more expensive than first admission (15)). AI can further assist in cost savings by assisting with resource allocation.
Considerations with AI-assisted Patient Discharge
The need for AI to analyze large volumes of patient data provides problems with patient data privacy and security. It is essential that the implementation of AI considers these issues and adheres to any confidentiality rules (16). In order to access sufficient information to assess a patient’s discharge needs, AI also requires integration into the existing IT systems. In addition, the electronic health records must be stored in a way that is compatible with AI (17). It is essential for medical staff to be trained in AI before using it (18). Improper use of AI could result in improper discharge from hospital which could then lead to an increased risk of readmission.
Finally, AI utilizes existing data to reach conclusions, consequently, if subsets are missing in the existing data then this may bias the data and AI may not reach the correct conclusions. For example, data obtained from mainly Caucasian populations may not be accurate when applied to populations with a high proportion of people of color (19).
Conclusion
Current issues surrounding patient discharge specifically delays in discharge and high rates of patient readmission, may be improved through the use of AI. AI can decrease discharge delays by streamlining administrative paperwork and using predictive analysis to identify those being discharged imminently, allowing processes to begin in advance of official discharge. In addition, AI can reduce readmission rates through increased patient engagement and understanding, and by predicting those at risk of readmission to allow resources to be focused on them. These processes not only make discharge more efficient but also save hospitals money. Certain aspects of AI use must receive careful consideration before implementation. However, companies have successfully begun to introduce these systems into hospitals with promising outcomes.
The full-length white paper on this topic provides additional details, including future trends in AI-assisted patient discharge and real-world examples of how implementation in hospitals is improving discharge systems.
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