Project Category : HEALTH CARE

Health Care

The health care industry is one of the largest industries in the world, and it has a direct effect on the quality of life of people in each country. Health care (or healthcare) is the diagnosis, treatment, and prevention of disease, illness, injury, and other physical and mental impairments in humans. Health care is delivered by practitioners in medicine, chiropractic, dentistry, nursing, pharmacy, allied health, and other care providers. The health care industry, or medical industry, is a sector that provides goods and services to treat patients with curative, preventive, rehabilitative or palliative care.

Artificial intelligence has been playing a critical role in industries for decades. AI has only recently begun to take a leading role in healthcare. According to Frost & Sullivan, AI systems are projected to be a $6 billion-dollar industry by 2021. A recent McKinsey review predicted healthcare as one of the top 5 industries with more than 50 use cases that would involve AI, and over $1bn USD already raised in start-up equity.  With such exponential growth, what does this mean for your organisation? How can you benefit the most from this game-changing technology?

When it comes to our health, especially in matters of life and death, the promise of artificial intelligence (AI) to improve outcomes is very intriguing. While there is still much to overcome to achieve AI-dependent health care, most notably data privacy concerns and fears of mismanaged care due to machine error and lack of human oversight, there is sufficient potential that governments, tech companies, and healthcare providers are willing to invest and test out AI-powered tools and solutions.

Let’s look at the Use-Cases where AI can bring the difference.

Managing Medical Records and Other Data:

Since the first step in health care is compiling and analysing information (like medical records and other past history), data management is the most widely used application of artificial intelligence and digital automation. Robots collect, store, re-format, and trace data to provide faster, more consistent access.

Treatment Design

Artificial intelligence systems have been created to analyze data, notes and reports from a patient’s file, external research and clinical expertise to help select the correct, individually customized treatment path.

Health Monitoring

Wearable health trackers like those from FitBit, Apple, Garmin and others monitors heart rate and activity levels. They can send alerts to the user to get more exercise and can share this information to doctors (and AI systems) for additional data points on the needs and habits of patients.

Medication Management

The National Institutes of Health have created the AiCure app to monitor the use of medication by a patient. A smartphone’s webcam is partnered with AI to autonomously confirm that patients are taking their prescriptions and helps them manage their condition. Most common users could be people with serious medical conditions, patients who tend to go against doctor advice, and participants in clinical trials.

Radiology

AI solutions are being developed to automate image analysis and diagnosis.  This can help highlight areas of interest on a scan to a radiologist, to drive efficiency and reduce human error.

Drug Discovery

To identify new potential therapies from vast databases of information on existing medicines, which could be redesigned to target critical threats such as the Ebola virus. This could improve the efficiency and success rate of drug development, accelerating the process to bring new drugs to market in response to deadly disease threats.

Patient Risk Identification

By analyzing vast amounts of historic patient data, AI solutions can provide real-time support to clinicians to help identify at risk patients. A current focal point includes re-admission risks, and highlighting patients that have an increased chance of returning to hospital within 30 days of discharge. Multiple companies and health systems are developing solutions at present based on data in the patient’s electronic health record, driven in part by increasing push back from payers on covering hospitalisation costs associated with re-admission. Other recent work has demonstrated the ability to predict risk of cardiovascular disease based purely on a still image of a patient’s retina.

Virtual nursing assistants

From interacting with patients to directing patients to the most effective care setting, virtual nursing assistants could save the healthcare industry $20 billion annually. Since virtual nurses are available 24/7, they can answer questions, monitor patients and provide quick answers. Most applications of virtual nursing assistants today allow for more regular communication between patients and care providers between office visits to prevent hospital readmission or unnecessary hospital visits. Care Angel’s virtual nurse assistant can even provide wellness checks through voice and AI.

AI-assisted robotic surgery

With an estimated value of $40 billion to healthcare, robots can analyze data from pre-op medical records to guide a surgeon’s instrument during surgery, which can lead to a 21% reduction in a patient’s hospital stay. Robot-assisted surgery is considered “minimally invasive” so patients won’t need to heal from large incisions. Via artificial intelligence, robots can use data from past operations to inform new surgical techniques. The positive results are indeed promising. One study that involved 379 orthopaedic patients found that AI-assisted robotic procedure resulted in five times fewer complications compared to surgeons operating alone. A robot was used on an eye surgery for the first time, and the most advanced surgical robot, the Da Vinci allows doctors to perform complex procedures with greater control than conventional approaches. Heart surgeons are assisted Heartlander, a miniature robot, that enters a small incision on the chest to perform mapping and therapy over the surface of the heart.

What are the challenges of AI in healthcare?

In order for an AI solution to be successful, it requires a vast amount of patient data to train and optimise the performance of the algorithms. In healthcare, getting access to these datasets poses a wide range of issues:

Patient privacy and the ethics of data ownership accessing personal medical records is strictly protected. In recent years data sharing between hospitals and AI companies has generated controversy, highlighting several ethical questions:

  • Who owns and controls the patient data needed to develop a new AI solution?
  • Should hospitals be allowed to continue to provide (or sell) vast quantities of their patient data even if de-identified to 3rd party AI companies?
  • How can patients’ rights to privacy be protected?
  • What are the consequences (if any) should there be a security breach?
  • What will be the impact of new regulations, like the General Data Protection Regulation (GDPR) in Europe – which includes a person’s right to have their personal data deleted in certain circumstances, with non-compliance generating what could be multi-million-dollar penalties?
  • Quality and usability of data in other industries, vast amounts of data is generally reliable and accurately measured (e.g. aircraft engine sensors or car location and velocity data to predict highway traffic).  In healthcare, data can be subjective and often inaccurate with issues including:
    • Clinician’s notes in electronic medical records are unstructured and can be difficult to interpret and process.
    • Data inaccuracy, a patient may be listed as a non-smoker but were they just reluctant to admit they had not been able to quit?
    • Data sources are siloed across many services providers making it difficult to capture a full profile and range of determinants for a patient’s health.