By: Vaibhavi Sahne RIG Intern Project Researcher

 

 

Figure 2: Future of AI in healthcare

Artificial Intelligence (AI) is a 65-year-old technology, which is a combination of a set of sciences, theories and techniques that also includes, statistics, computational neurobiology, computer science, mathematical logic, and probabilities. In the immediate future, AI language is looking like the next “big thing”. In fact, it’s already underway. When was the last time you called a company and directly spoke with a human?

It’s clear that connecting a human brain with a computer network via an implant could, in the future, open up the distinct advantages of machine intelligence, communication, and sensing abilities to the individual receiving the implant. For some, brain–computer interfaces are perhaps a step too far —particularly if the approach means tampering directly with the brain. The first quarter of 2021 saw a new funding record with nearly $2.5 billion raised by startups focusing on AI in healthcare, according to CB Insights.

AI development is shifting from being model-centric to being data-centric. This includes improving the quality of the data used to train AI programs and building the tools and processes required to put data at the center of developers’ work. The solution for mental health, diagnostics, and the operational side of healthcare is still being researched. AI can help in ensuring that medical procedures are carried out correctly, and that chronic patients are cared for, at home or in the clinic, in a timely fashion.

The following case study shows the utilization a Computerized Provider Order Entry (CPOE) System preventing hospital medical errors and adverse drug events:

Computerized provider order entry (CPOE) systems allow physicians to prescribe patient services electronically. In hospitals, CPOE essentially eliminates the need for handwritten paper orders and achieves significant cost savings through increased efficiency. This case study examines the benefits and barriers to CPOE adoption in hospitals to determine the effects on medical errors and adverse drug events (ADEs) as well as examine cost and savings associated with the implementation of this newly mandated technology. CPOE systems in hospitals were found to be capable of reducing medical errors and ADEs, especially when CPOE systems are bundled with clinical decision support systems designed to alert physicians and other healthcare providers of pending lab or medical errors. However, CPOE systems face major barriers associated with adoption in hospital systems, mainly high implementation costs and physicians’ resistance to change.

Approximately 200,000 people die every year in the United States as a result of preventable medical errors. The majority of medical mistakes happen when the physician orders incorrect services and prescriptions for the patient. Another 770,000 patient injuries and deaths are due to adverse drug events (ADEs). If the pharmacist is not able to read a prescription handwritten by the physician, the patient is at risk of ADEs. A CPOE system may be the solution to decrease the number of ADEs in a hospital, enhance patient safety, and decrease preventable medical errors. In addition, CPOE, a software system designed to be utilized in a hospital, has the ability to resolve other problems in a hospital setting, such as removing abbreviations and acronyms and increasing order speed through the use of electronically ordered services and prescriptions.

After the CPOE system has been adopted, the process includes an assessment of the benefits and barriers to the use of CPOE, and the process starts over so that the barriers can be addressed and the benefits assessed (see Figure 1). The use of this conceptual framework in the present study is applicable because the focus of both studies is to show how new technologies can be applied to healthcare settings to improve the care of patients.

Figure 1: Research Framework for the Study of CPOE Adoption in Healthcare

There are barriers for implementing CPOE such as, system interoperability, faulty programming, and system crashes. Although, the main barrier to implementing CPOE has been cost. The mandates for CPOE is fairly new. However, a limited number of facilities have fully adopted and implemented the system thus limiting the amount of useful searchable publications relative to its effectiveness. Performing a systematic review with stringent criteria and measuring the effect of sources or weighing the sources for complete accuracy, relevance, and reliability was out of the scope of this review, given the highly dissimilar qualities of the data.

For practical implementation of CPOE, the adoption and implementation of a CPOE system can be a prolonged process because of physician and staff resistance to the new system and some technical barriers. Training needs to be available for all authorized personnel using the system, in particular for physicians. Hospitals are open 24 hours a day, seven days a week, so the hospital employees need to know and fully understand the system. Providing 24/7 technical support for weeks after the system goes live is necessary. Thus, CPOE systems have the potential to be an effective solution for limiting hospital medical errors and ADEs experienced in the United States. CPOE adoption can facilitate the reduction of medical errors and ADEs as well as creating cost savings in hospitals. CPOE also supplies providers with additional clinical knowledge and patient-related information that is intelligently filtered and presented at appropriate times.

By 2030, AI will access multiple sources of data to reveal patterns in disease and aid treatment and care. Healthcare systems will be able to predict an individual’s risk of certain diseases and suggest preventative measures. AI will help reduce waiting times for patients and improve efficiency in hospitals and health systems. Hence, the future of AI in healthcare will transform the state of the healthcare industry. It will not only save time, money and resources of both doctors and patients, but it will also greatly simplify the entire process towards curing patient’s diseases.

 

 

 

 

References:

1.https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/

2.https://www.technologyreview.com/2016/11/10/156141/the-future-of-artificial-intelligence-and-cybernetics/

3.https://www.forbes.com/sites/gilpress/2021/04/29/the-future-of-ai-in-healthcare/?sh=1e26e810163b

4.https://www.softwareadvice.com/resources/future-of-ai-in-healthcare/

5.https://www.weforum.org/agenda/2020/01/future-of-artificial-intelligence-healthcare-delivery/

  1. Case study: https://bok.ahima.org/doc?oid=300744#.YYcC_WDMLIU