Three Ways Artificial Intelligence And LIDAR Can Be Used In Healthcare Delivery


The healthcare sector is currently experiencing a crisis. Unfavorable patient outcomes are a result of a combination of burnout, soaring expenses, and a reactive approach to healthcare. Healthcare organisations are under tremendous financial stress as a result of the impact. Due to an increase in hospital readmissions, Medicare will penalise 2,499 institutions in the fiscal year 2022. Each Medicare patient will experience an average payment reduction of 0.64 percent as a result of the fines, which will total $521 million for all hospitals combined.

The financial viability of healthcare institutions is being crippled by some of the most preventable events, such as hospital infections, patient falls, and other avoidable medical issues, which are also seriously harming or killing people.

By delivering proactive care, new technologies like artificial intelligence (AI) and light detection and ranging (LIDAR) techniques can enhance patient outcomes and reduce expensive penalties. Additionally, it is crucial to equip care teams with technology to increase efficiency and reduce tedious chores that cause fatigue due to the Covid-19 pandemic’s escalation of clinician stress.

Why AI And LIDAR Are Here To Stay In Healthcare

Everyday items like smartphones, smart TVs, and speakers, as well as video doorbells and voice-activated appliances, all contain artificial intelligence (AI). All of these advancements, from what restaurant to eat at this evening to what to watch next on your preferred TV streaming service, are made possible by AI, which enables data interpretation and educated decision-making.

Given how pervasive AI is in our daily lives, it only makes natural that it might support our healthcare requirements. Fortunately, business leaders in the healthcare industry are embracing AI. According to the findings of the “3rd Annual Optum Survey on AI in Health Care,” 83 percent of healthcare executives already have an AI strategy in place, and another 15% intend to do so. 59 percent of respondents to the poll anticipate “concrete cost savings within three years,” a 90 percent increase since 2018.

LIDAR is an advancing disruptive technology, too. For instance, in the auto industry, some self-driving vehicles use LIDAR technology to recreate the road in three dimensions so the vehicle can precisely recognise people, vehicles, and other things in the immediate vicinity with millimetre accuracy. In the healthcare sector, LIDAR and AI can be used to increase data predictability for proactive patient care.

Here are three applications for AI and LIDAR in healthcare delivery:

1. Building A Preventative Care System

Instead of focusing on preventative care, our healthcare system is more concerned with “ill care.” Consumers rarely interact with their healthcare professional before a health occurrence, on average. This is mostly because of the prevalent fee-for-service payment model, which is based on a reactive approach to care.

AI can assist in bridging the gap between reactive and proactive care. Many decisions in the healthcare industry involve sorting through countless data points, like vital signs. AI can rapidly comprehend and decode the data, enabling earlier diagnosis of negative outcomes like a stroke or infection.

AI-powered sensors that identify patients at risk of falling can help stop other unnecessary incidents, such as patient falls. Each year, 850,000 hospital patients in the United States suffer catastrophic injuries after falling. Artificial intelligence (AI) technology can evaluate a patient’s stride, balance, and function, reducing the likelihood of harmful falls. A patient who is at risk of falling can be accurately and proactively predicted by AI and LIDAR to get up from their chair or bed.

2. Sustaining Financial Viability

The expense of “never occurrences,” such patient falls and hospital-acquired illnesses, while a patient is in the hospital, is astonishing. The Centers for Disease Control and Prevention (CDC) researchers estimate that the annual cost of hospital-acquired illnesses in the United States is over $28 billion. Better outcomes and the prevention or reduction of expensive penalties for readmission or “never events” may result from proactively alerting care teams before an event occurs using tools like artificial intelligence (AI). This can help safeguard the financial viability of the healthcare organisation.

3. Supporting An Overburdened Workforce

The Covid-19 pandemic has increased burnout, particularly with nurses. In reality, according to a December 2020 International Council of Nurses study, 90% of National Nurses Association respondents said they were “very or extremely concerned” that high patient loads, inadequate funding, burnout, and stress were to blame for nurses abandoning their jobs.

To free up the nurses’ time to concentrate on patient care, healthcare companies can use AI solutions that can handle “busy work” chores like food orders. Another stressor that contributes to burnout is false alarms. According to research, 80 to 99 percent of alerts are false, which forces nurses to frantically search the hospital floor for a needle in a haystack. If nurses are given the tools that take use of LIDAR’s intelligence, which produces high-fidelity data, they can reduce the burdens and stress associated with false alarms.

The time has come for healthcare businesses to take into account AI and LIDAR to assist enhance outcomes, expedite care, and safeguard their financial sustainability. AI is a potent tool that may be utilised to precisely and accurately absorb and evaluate data, assisting physicians in making the best choices for better patient outcomes.

Next Post

causaLens partners with Mayo Clinic to use causal AI to discover cancer biomarkers

Using its Causal AI technology, causaLens, a deep tech company based in London that is reshaping the future of AI, is taking a significant step toward the adoption of non-invasive methods for cancer detection and understanding. According to recent study, Causal AI provides accurate early cancer detection using quick and […]