Assistive technology 1: Predictive AI

Artificial intelligence is revolutionising the workplace, in a similar way to how to steam engines revolutionised factories during the industrial revolution.  Although AI is unlikely to replace workers just yet, it is possible that by adopting artificial intelligence technologies workers are likely to be able to increase their productivity, accuracy or consistency in their day-to-day activities.

The adoption of artificial intelligence in the workplace is still in its early phases so it is challenging to determine the full impact that it will have, but it is likely to be widely used in almost every workplace in your lifetime.

In this step we will explore the types of artificial intelligence that are becoming widely used in different work places today. We will look at some specific roles and how artificial intelligence is changing the way the people work using AI as an assistive technology.

We will start with predictive analytics, and then explore generative AI and conversational AI in the proceeding steps.

Predictive analytics

Predictive analytics is a branch of advanced analytics that uses historical data and machine learning techniques to make predictions about future events or outcomes. It aims to forecast what is likely to happen based on patterns and trends discovered in the historical data. Predictive analytics is used in various industries and applications, such as business, finance, healthcare, marketing, and more.

Predictive analytics is increasingly being used in healthcare to improve patient outcomes, enhance operational efficiency, and reduce costs. It uses historical patient data, medical records, and other sources to make predictions about future health events or outcomes.

Here’s how predictive analytics is applied in healthcare:

Disease risk assessment:

• Identifying high-risk patients: Predictive analytics can help healthcare providers identify patients at high risk of specific diseases or conditions. By analysing patient data, including medical history, genetics, and lifestyle factors, it can flag individuals who are more likely to develop diseases like diabetes, heart disease, or cancer.
• Preventive Interventions: Healthcare providers can use this information to implement preventive interventions, such as lifestyle counselings, regular screenings, or early treatment, for high-risk patients. This proactive approach can reduce the frequency and severity of diseases.

Hospital readmission prediction:

• Reducing readmissions: Predictive analytics can predict the likelihood of a patient being readmitted to the hospital shortly after discharge. By identifying high-risk patients, healthcare organisations can provide better post-discharge care, ensuring that patients receive the necessary support and follow-up to prevent readmission. This reduces healthcare costs and improves patient outcomes.

Resource allocation:

• Optimising resource allocation: Predictive analytics can assist healthcare facilities in optimising resource allocation. By forecasting patient admissions, emergency room traffic, and required staffing levels, hospitals can allocate resources more efficiently, ensuring that they have the right number of staff and beds available at the right times.

Chronic disease management:

• Personalised care plans: Predictive analytics can aid in creating personalised care plans for patients with chronic conditions like diabetes, asthma, or hypertension. By analysing patient data, healthcare providers can tailor treatment plans and monitor patient progress more effectively.

Fraud detection and claims processing:

• Identifying fraudulent claims: Predictive analytics can assist insurance companies and healthcare providers in identifying fraudulent insurance claims. By analysing claims data, it can flag unusual patterns that may indicate fraud, ultimately reducing costs and preventing misuse of resources.

Emergency department wait times:

• Reducing wait times: Predictive analytics can predict patient inflow and acuity levels in the emergency department, enabling hospitals to better manage resources and reduce patient wait times, thus improving the quality of care.

Image analysis:

• Diagnosis and early detection: In medical imaging, predictive analytics can assist radiologists in the early detection of diseases, such as cancer, by identifying patterns or abnormalities that may be missed by the human eye.

Impact of predictive analytics in healthcare

• Improved patient outcomes: By identifying at-risk patients and providing targeted interventions, predictive analytics helps improve patient outcomes and reduce disease progression.
• Cost reduction: Preventing readmission, optimising resource allocation, and reducing fraud can significantly reduce healthcare costs for both providers and payers.
• Enhanced efficiency: Healthcare organisations can operate more efficiently by using predictive analytics to optimise their processes and resource allocation.
• Personalised care: Patients receive more personalised care plans, leading to better treatment outcomes.
• Early intervention: Early detection of diseases and complications enables timely medical intervention, potentially saving lives.
• Patient satisfaction: Reduced wait times, improved access to care, and better service quality contribute to higher patient satisfaction.

Overall, predictive analytics is transforming healthcare by enabling more proactive, data-driven decision-making that benefits both healthcare providers and patients.

Now let’s move on to look at how generative AI is creating content, images and information in the workplace.