Today, health care organizations have access to more information than ever before—from emergency department records to radiology reports to cases of surgical errors and complications. Yet the data alone isn’t enough to transform your operations. The data needs to be formatted, stored, and organized in a way that is meaningful and actionable for the organization. It is only after this, tools like Artificial Intelligence (AI) and machine learning can be used to make sense of all the information that exists and truly inform service and clinical planning.
Understanding the Potential of AI
In layman’s terms, AI is a form of technology that analyses data and uses algorithms (or a series of steps) to identify patterns. Most people have experienced AI while browsing online. Think of when Google shows you ads targeted to your interests, or when Facebook identifies a person by their photo. In health care, these and many other advanced capabilities can go a step further to help clinicians and administrators make educated decisions on everything from increasing diagnostic and treatment capabilities to using supplies and manpower more efficiently.
The logistics of how AI informs medicine can be difficult to grasp for people who haven’t been exposed to or trained in data science. Further, data scientists are limited in what they can accomplish without having input from medical experts.
The Need for Diverse Data Science Teams
The best way to realize the full potential of data science in the health care sector is by having leaders, clinicians, and data scientists work collaboratively in multi-disciplinary data science teams so they can pool their knowledge, experiences, and perspectives.
For ideal results, you would like to have a core data science group that has the necessary expertise. For example, the group must include someone who is good at understanding both the clinical context and the technical requirements of what can (and cannot) be done
Having diverse racial and cultural representation on your data science team is also essential to prevent perpetuating stereotypes by not looking at an inclusive picture when it comes to collecting and analysing data.
While the value of having a data science team is clear, getting busy health care professionals and administrators to participate can be challenging in some organizations. The benefits people can expect in return for getting involved may provide strong motivation.
Finding the Best Healthcare Data Science Solution for Your Needs
How your data science team operates typically will depend on the size of your organization and the resources at hand, as well as the extent of your goals. For instance, you may find your organization has challenges with scheduling. Can you improve appointment times using AI? Or your radiology team may be unhappy with missed diagnoses. Can you find software to help radiologists hone their accuracy? Or the marketing team needs to reach more dermatology patients. How can you let more people know your services exist? Where does AI fit in? These are the kinds of questions that leaders need to be able to answer.
In many organizations, it will come down to one of the following possible scenarios on how to operationalize your efforts:
- Hospitals can develop their own AI solutions and involve clinicians to advise the creation and make sure they will achieve their goals.
- They can partner with established vendors or big tech companies to use pre-existing solutions.
- They can look to other organizations that have an effective data science strategy and use the products and lessons learned to guide their own efforts.
For smaller hospitals and organizations, partnering with vendors or with other health systems is usually the most feasible approach rather than trying to start from scratch. But each organization needs to determine what will work best for their situation.
The Need for Human Expertise to Guide AI Efforts
Health care is full of complex issues. Data can offer assistance to unravel them, but the data contained within the information is usually hidden. In this manner, we require data specialists working in a group to discover and apply the bits of knowledge. You can’t construct a solution to move forward unless you know what information is required and how it can be utilised.
AI and cognitive computing are projected to empower patients, transform the practice of medicine, and save the health care industry over $150 billion by 2025.It is estimated that if implemented correctly, AI could improve health outcomes by up to 40 percent and reduce treatment costs up to 50 percent by improving diagnosis, increasing access to care and enabling precision medicine.
There’s no denying that AI is long haul of wellbeing care, be that as it may AI advances won’t execute themselves and require significant translational ability to convey on their guarantee. Although wellbeing care experts have firsthand involvement with wellbeing and organizational issues, they regularly don’t have a point-by-point understanding of the AI innovation required to address them. Numerous medical professionals know actualizing AI will progress their organization and keep them competitive but are anxious about the innovation and scale of information. On the other hand, data science and technical experts are not across the complexities of medical care that is necessary to adopt AI in healthcare planning and delivery.
Solving Healthcare Challenges with AI
There is a colossal amount of data being collected in the health care world – and much of it has not been fully utilized to best support care due to its complexity and scale. With the growing role of AI in health care organizations, it can be used to harness this data to help clinicians and leaders make expedient, informed, and personalized decisions when treating patients. The health care field is just starting to understand the depth and range of improvements that AI can make – and this is only the beginning of its incredible impact on improving the public’s health.
There are many ways AI is helping overcome long-standing health care challenges:
- Diagnosis: AI can process complex images, like CT scans, along with health records to make an accurate diagnosis in near real-time. A 2019 study found that AI correctly diagnosed diseases 87% of the time when reviewing medical imaging, compared to 86% by health care professionals. By combining the AI skill set with that of clinicians, the rate of misdiagnoses goes down, also helping reduce physician overload and in turn improving productivity.
- Precision Medicine: AI has played a substantial role in the emerging field of precision medicine, which defies the one-size-fits-all approach to health care. Precision medicine is heavily based in data, taking into consideration a patient’s behaviours, environment, genome, and medical history to develop a more personalized treatment plan. AI helps manage the massive data sets used to inform this approach, allowing clinicians to better understand the patient, provide more specialized care, and more efficiently target resources. This has ultimately been proven to better treat disease and improve patient care.
- Prediction Models: By using prediction models, clinicians can identify how a patient compares to others with a similar diagnosis, helping calculate potential outcomes. For example, it can help when determining if a patient is at higher risk of death, may need extra support to prevent complications, or can be released from the hospital shortly.
However, evidence also shows AI comes with risks such as algorithmic bias. As individuals who develop AI carry implicit bias and health care systems exist in societies with prejudice, these biases end up being reflected in algorithms. It is crucial to think proactively about bias when developing and implementing AI by taking strategic actions to minimize the risk of algorithmic bias to ensure AI is helping – not further harming – the communities it serves.
Healthcare IT companies that are eager to expand their business will find growth opportunities in:
- Applying AI on imaging to drive differential diagnosis, which was not possible with legacy systems. They can also identify regional disease hot spots through smart assessments of historical healthcare utilization data;
- Combining patient-generated data with academic evidence to create personalized treatment options;
- Employing clinical documentation improvement to allow providers to help physicians and coders reduce individual burn-out. CDI’s impact on claims and denial management is critical; and
- Employing AI-powered revenue cycle management platforms that seamlessly interface with providers’ incumbent payer mix and auto-adjust claims content based on each payer’s coding and reimbursement criteria.