Medical informatics is found at the intersection of healthcare and technology.
It is where skills in both medical and computer sciences come together in an effort to improve healthcare and patient outcomes. Professionals in this hybrid field draw on expertise from both disciplines to put technology to its best use in patient care, clinical and research settings.
Specialty areas include:
- Bioinformatics: Practitioners in this specialty are concerned with storing, retrieving, sharing and helping analyze biomedical information for research and/or patient care. Subspecialties include chemical, nursing and dental informatics.
- Public health informatics: This specialty involves the use of technology to guide how the public learns about health and health care while also ensuring access to the latest medical research. Professionals also ensure public health practices have access to the information they need.
- Organizational informatics: The focus here is ensuring a smooth flow of communication within a healthcare organization.
- Social informatics: These specialists study the social aspects of computer science while gaining insights into how information technology affects social environments and how social environments affect information technology.
- Clinical informatics: This is the application of informatics and information technology for clinical research and patient care. Professionals leverage information technology for medical education, patient education and students, among others.
Microsoft unveiled the details of its latest effort as part of its $165 million AI for Good program. The company plans to spend $40 million over the next five years to support research and public health efforts under its new AI for Health initiative.
The plan includes giving nonprofits and academic researchers access to the company’s AI tools, cloud computing resources, and collaboration with the company’s data scientists. Certain projects will also be able to receive cash grants.
Neuroinformatics is a research field concerned with the organization of neuroscience data by the application of computational models and analytical tools. These areas of research are important for the integration and analysis of increasingly large-volume, high-dimensional, and fine-grain experimental data. Neuroinformaticians provide computational tools, mathematical models, and create interoperable databases for clinicians and research scientists. Neuroscience is a heterogeneous field, consisting of many and various sub-disciplines (e.g., cognitive psychology, behavioral neuroscience, and behavioral genetics). In order for our understanding of the brain to continue to deepen, it is necessary that these sub-disciplines are able to share data and findings in a meaningful way; Neuroinformaticians facilitate this.
Neuroinformatics has grown because it has become an important part of the solution to several problems with describing, preventing, diagnosing and treating patients with neurologic disease. In addition, neuroinformaticians are an increasingly integral part of drug discovery and development for diseases like Alzheimer's disease (AD), Parkinson's disease and multiple sclerosis. For example, the failure rate of AD drug development is 99% ; the failure rate of the development of disease-modifying therapies for AD is 100%. The process is sorely in need of a rethink.
Consequently, almost every major pharmaceutical and medical device company is integrating informatics and remote sensing into their product development. Here' an example of how one company offers experiential learning to data scientists.
But, getting ideas to patients with incurable neurologic disease will require more than clinicians who understand how to work with data or non-clinicians who have some understanding of neurologic disease. It will also require clinical neuroentrepreneurs, including doctors, pharmacists, nurses and public health practitioners who have the knowledge, skills, attitudes and competencies to complete the workforce three legged stool as new product teams strive to create clinically valid new treatments Unfortunately, it is unlikely that medical students, residents and fellows will learn these things in their formal training. That might change if and when we create entrepreneurial medical schools.
A third of American hospitals and imaging centers are already implementing artificial intelligence, machine learning or deep learning in radiology.
About another third plan on adopting it within the next two years, according to respondents to a survey, the results of which were recently released by an industry research organization. Many of those applications apply to neurologic disease like brain tumors ,traumatic or toxic brain injury or progressive neurologic disease.
Future developments in information and communications technologies, medical informatics training programs and bioentrepreneurship education will create a pipeline of interdisciplinary investigators and clinicians. Neuroinformaticians, though, won't be able to solve the neuroproblems alone, no matter how brainy they are.
Arlen Meyers, MD, MBA is the President and CEO of the Society of Physician Entrepreneurs on Twitter@ArlenMD , Co-editor of Digital Health Entrepreneurship and an advisor to Cerescan and Neosoma.