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How Deploying AI in Hospital Systems Helps Reduce Medical Errors

According to the WHO, medical errors and faulty medical diagnoses are part of the top global reasons patients die, with 1 in every 10 hospital admissions resulting in a medical error, and 1 in 300 admissions resulting in death as a direct result of these medical errors.

Doctors and paramedics use electronic health records (EHRs) and electronic medical records (EMRs) as their main sources of patient information and data. Medical history, patient’s symptoms, and other details specific to the patient’s health are stored on the EHRs and EMRs.

However, today’s EHR systems for large, integrated healthcare delivery networks are typically complex, cumbersome, expensive to maintain and customize, inflexible, and not very user-friendly. In most cases, they are sourced from commercial vendors and require considerable time, investment, and consulting assistance to deploy, support and optimize.

Despite this, these systems are seldom a good fit with the hospital’s preferred care delivery processes. As delivery networks expand and implement broad enterprise EHR platforms, the demand to make these systems more of a help than a hindrance to clinicians is increasing.

Medical professionals want to spend more time helping people than working with admin systems.

Additionally, in the U.S., regulatory, billing and revenue cycle requirements are further increasing complexity to the electronic healthcare workflow, potentially taking even more time away from clinicians to engage with patients.

It is widely believed by medical professionals that integrating artificial intelligence (AI) into EHRs and EMRs will dramatically impact the medical industry. With the amount of structured and unstructured data contained in EHRs, it follows that AI technology, which is fed by big data, is the logical go-to for medical professionals looking to sort and analyze vast amounts of information.

What is Artificial Intelligence in Healthcare?

Artificial intelligence (AI) falls within the field of computer sciences. It is comprised of computerized applications that utilize algorithms and software to imitate human intelligence in scrutinizing and understanding complex information and data.

In the medical industry, AI technology has been developed to support medical processes such as diagnosis, treatment protocol development, drug development, personalized medicine, patient monitoring and care and various others. Additionally, as a result of better diagnosis and disease predictions, the implementation of big data analytics and AI in hospitals could lead to over 25% savings in annual costs by lowering hospital readmission rates, among other factors.

How AI May Make Medical Errors a Thing of the Past

As we now stand, there is a worldwide critical skills shortage of over 7 million medical and healthcare professionals with no sign of improvement.

Healthcare professionals are feeling the weight of an ever-increasing demand for services with a diminished workforce and capacity to provide them. This is where AI technology steps in to support and help overcome these challenges.

Clinical documentation and data entry

In this context, AI is unquestionably the best way forward for hospitals and medical institutions to eradicate these medical errors and create a safer, more efficient healthcare system. Additionally, capturing clinical notes with natural language processing (NLP) frees up healthcare professionals to focus on their patients rather than data capture.

According to a recent study, AI can potentially reduce medical errors by 30-40% and reduce treatment expenses by as much as 50%.

AI’s NLP capabilities mean less time is spent behind a screen and keyboard, and more time can be spent with what matters most. When it comes to EHR systems, AI is not only improving data discovery, data extraction, personalized treatment recommendations, and freeing up medical professionals to focus on their patients, it is also making EHRs more intuitive and user-friendly. This is a critical step in the evolution of EHRs, as they move away from being the complicated, difficult to use systems that are often cited as contributing to clinician burnout.

1. Early detection and proactive treatment measures

Because of AI’s ability to quickly evaluate seemingly unconnected raw data, find patterns and predict the likelihood of these patterns recurring, AI tools like SEDGE help medical practitioners stay one step ahead of potentially chronic diseases, diagnose quicker and allow for the early administering of treatment.

SEDGE can be used to build prediction models from big data to alert clinicians of high-risk conditions such as sepsis, stroke, seizures, and heart failure. SEDGE predictive analysis helps identify patients who are more at risk and those who are most likely to respond to treatment protocols. These could be integrated into EHRs to provide decision support.

2. Automated processes

AI provides a welcomed alternative to the burden of clinical documentation tasks experienced by most healthcare providers. AI-powered platforms can automate mundane, time-consuming, repetitive processes such as result notifications, medication refills, and reporting.

Hospital staff can use voice commands, video recordings, and photos to capture essential data, which is then automatically processed and loaded into the system. Additionally, AI can alert staff on tasks that require human attention.

3. Amplified medical reach to

developing countries

The use of AI in healthcare can also solve the challenges experienced by those in developing nations of lack of accessibility and the high cost of medical services. Paramedical staff in low-source areas can use AI-powered technology or devices to carry out tests which would typically have required a trained diagnostic expert on site.

Additionally, AI can quickly, affordably, and accurately process the patient’s medical information and identify appropriate treatment options.

Gain the benefits of AI in healthcare today

SEDGE, an AI solutions provider based in Atlanta, USA, aims to augment medical decision-making through intelligent, accurate data analytics and predictive analytics.

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