Artificial Intelligence in Healthcare Administration

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Table of Contents

Background
Gap Description
Detailed Evaluation Plan
References

The current healthcare state is defined by the complexity and rise of data, which has necessitated the introduction of artificial intelligence (AI). In many healthcare facilities, healthcare administrators are tasked with roles ranging from ensuring better patient experiences, patient record keeping and other responsibilities such as designing budgets. Therefore, it is imperative to integrate AI components, such as machine learning, in policy and decision-making among different administrative roles (Lee, 2022). The following executive summary centralizes the application of AI in healthcare administration.

Background

Healthcare administration or non-patient care tasks entail all the work performed behind the scenes to ensure patients gain better healthcare experiences. Additionally, the administrative process deals directly with budgets and policies, ensuring the healthcare providers’ welfare is met, and their safety is warranted (Lee, 2022). Some of the key responsibilities this arm of healthcare is tasked with include developing staff and physician schedules, managing finances, improving facility quality and efficiency, training staff and ensuring law compliance.

Gap Description

Healthcare Issue Description

As described earlier, the over-reliance of people without appropriate technology in executing healthcare administrative roles often translates to bureaucracy, resulting in hospital inefficiencies. With hospital inefficiencies, the organization’s workflow is interfered with, leading to huge hospital losses. According to Forehand et al. (2021), about $1.75 million per hospital is lost in the US due to inadequate communication between hospital administrators. This figure translates to $11 billion in losses in the US healthcare industry (Forehand et al., 2021). Besides ineffective communication, most hospitals lacking AI systems experience inefficiencies such as document duplication, poor patient flow, inappropriate hospital admissions and length of stay and incomplete drug reconciliation.

Stakeholder Groups Impacted by the Issue

The main stakeholder groups affected by healthcare inefficacies from the organization administration are patients, physicians and nurses. Healthcare inefficiency affects patients’ flow from one department to another or even in and out of the hospital. Most patients succumb after undergoing long waiting periods for their surgeries and treatments. Besides, physicians face the challenge of document duplication in a case where there are inefficiencies in the documentation methods (Lee, 2022). For instance, physicians might find duplicated charts or electronic records, which means much time might have been wasted on a patient that required minor treatment.

Responsible Stakeholder in Addressing the Issue

The key stakeholders in addressing healthcare inefficiencies in the administrative processes include the government, hospital administrators and the direct-patient contact staff. The government should fund technological projects such as robotics and neural work, which aim to reduce hospital administration’s bureaucracy and complexity (Lee, 2022). Besides, hospital administrators should redesign system-wide processes that streamline patient flow in the organization. Lastly, direct-contact staff, such as nurses, should be familiar with communication methods such as texting and medical records.

Research Related to the Gap and New Service Proposal

Healthcare inefficiency can translate to huge losses, meaning prompt strategies should be implemented to counter it. It is estimated that inadequate communication resulting from a lack of secure text messaging in healthcare can lead to a loss of $875,000 per hospital (Mintz & Brodie, 2019). Therefore, it is important to introduce AI in a hospital setting to offset the present inefficacy. AI has five subsets that are imperative to healthcare administration: neural network, machine learning, deep learning, robotics and computer vision. This technology can improve communication by catching subtle cues to precisely analyze the presentation methods. The implementation of the AI technology has been outlined in Table 1.

Detailed Evaluation Plan

Table 1. Evaluation Plan for the Implementation of AI in Healthcare Administration

Strategic Objectives	Key Initiatives (1-to-3-year initiatives)
Integrate machine learning in voice-to-text transcription	Partner with companies such as RapidMiner to understand machine learning
Introduce neural network in identifying patient records	Use Python libraries such as NumPy to acknowledge the working of neural network
Introduce computer vision in gathering important data from patient records	Establish a workgroup that to learn about computer vision through tools such as OpenCV

References

Forehand, C., Fitton, K., Newsome, A., Smith, S., Jun, A. H., & Maddox, S. (2021). 868: Characterizing documentation of pharmacist productivity in the intensive care unit.Critical Care Medicine, 49(1), 431. Web.

Lee, J. H. (2022). Special issue “artificial intelligence in oral health.”Diagnostics, 12(8), 1866. Web.

Mintz, Y., & Brodie, R. (2019). Introduction to artificial intelligence in medicine.Minimally Invasive Therapy & Allied Technologies, 28(2), 73-81. Web.

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