Revolutionizing Admissions: AI/ML-Enhanced Process of Inpatient Psychiatric Wards
Authors: Manesh S. Shelley Simkins
Background
Admitting patients with mental health or behavioral challenges into inpatient psychiatric wards often involves a complex, document-intensive pre-admission process. This process requires careful screening to ensure adherence to admission criteria, which include evaluations of the patient's physical and mental state, risks to themselves or others, and the appropriateness of inpatient care over alternative treatments.
In today’s world, this process is completed via faxing large amounts of documentation to the potential receiving facility, and multiple phone calls to verify information and receive updates. The reliance on fax and phone calls often results in delays, errors, and inefficiencies that can compromise patient care.
Challenges
The primary challenges in this process are the timely gathering and verification of required documentation, the assessment of patient eligibility based on this documentation, and the communication between multiple parties including healthcare providers, insurance companies, and legal guardians or family members.
Documentation Requirements
The paperwork typically includes a referral from a health professional, a detailed medical evaluation, consent forms, and insurance documentation. These documents are sent to the admissions office of the psychiatric facility by the referring physician or mental health professional. In some cases, additional legal documents might be required, especially if the admission is involuntary.
AI/ML Application
Natural Language Processing (NLP): Function: Automates the extraction and analysis of text from admission documents that come from forms in faxes and other sources. Problem Solved: Speeds up the review of extensive documentation, ensuring quick and accurate information extraction. Research suggests that implementing NLP can reduce document processing time by up to 60%.
Predictive Analytics: Function: Utilizes historical data to predict patient outcomes and suitability for inpatient care. Problem Solved: Enhances decision-making by predicting risks and needs, thus optimizing patient matching to appropriate care settings. Predictive models have been shown to improve patient placement accuracy by 30%.
Machine Learning Algorithms (Multi-layer Perceptron Classifier) Function: Assesses eligibility and risk by analyzing complex datasets involving patient history and current evaluation metrics. Problem Solved: Provides a systematic approach to determine the appropriateness of inpatient treatment, reducing subjectivity in assessments. MLPs, a type of neural network, are especially effective in classification tasks where patterns in data are less obvious.
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Admission Rules and Criteria
The criteria for admission are typically derived from a combination of state laws, medical board guidelines, and individual hospital policies. These are often documented in internal hospital protocols and regulatory frameworks.
Implementation
By deploying AI-driven decision support systems, psychiatric facilities can apply admission criteria systematically and consistently. These systems ensure that the evaluations are based on comprehensive data analysis, thereby minimizing human error and potential biases in patient selection.
About the Authors:
Manesh S. is the Managing Director of AI/ML Transformation Services at ProNexus Advisory , and the founder of Phronesis AI, phronesis-ai.com. With over 20 years of expertise in healthcare strategy, operations, and technology, Manesh excels at bridging the gap between cutting-edge solutions and real-world healthcare needs. He possesses a deep understanding of the value chain within hospitals, health insurance, and other healthcare industry participants, adept at identifying opportunities for AI/ML to enhance operations and develop new clinical operating models through automation and advanced analytics. Manesh drives breakthroughs in performance and business/clinical outcomes, helping organizations unlock new efficiencies and improve patient care.
Shelley Simkins is the Managing Director of Healthcare Technology at ProNexus Advisory and the founder of Write2Solutions. Shelley leverages her more than 15 years of clinical leadership to bridge the gap between healthcare providers and technology solutions. Her ability to understand the intricacies of healthcare workflows allows her to tailor technology implementations to seamlessly integrate with existing systems, ensuring a smooth transition and maximum utilization of resources. Shelley is at the forefront of navigating the evolving landscape of healthcare technology. Her commitment to staying informed about emerging technologies and industry regulations positions her as a trusted advisor to healthcare organizations seeking to enhance their workforce management capabilities.