It’s difficult to wrap our minds around the blistering pace of innovation that is about to come. – Eric Gastfriend
Ninety percent of all scientists that have ever lived are alive today according to Eric Gastfriend for the Future of Life Institute. He says, “If science is growing exponentially, then the major technological advancements and upheavals of the past 200 years are only the tip of the iceberg.”
The medical Internet of Things and Big Data in healthcare
Also growing exponentially are connected devices collecting data. The Medical Internet of Things (mIoT) is already collecting massive amounts of Big Data in healthcare. According to a paper published by Dimiter V. Dimitrov MD PhD for the Korean Society of Medical Informatics, technologies can now reduce overall costs for the prevention or management of chronic illnesses as many patients use mobile apps and smartphones. These devices can constantly monitor health indicators, auto-administer therapies, or track real-time health data when a patient self-administers a therapy. Research that might have taken two years can now be done in two months.
— Health IT Central (@HealthITCentral) November 21, 2016
Dimitrov says mIoT is a critical piece of the digital transformation of healthcare, “It allows new business models to emerge and enables changes in work processes, productivity improvements, cost containment and enhanced customer experiences.”
The Centers for Medicare & Medicaid Services (CMS) data system
Big Data is defined by the three V’s: Volume, Variety and Velocity. It includes vast amounts of data, significant heterogeneity in the type of data, and an ability to be quickly accessed and analyzed. Others add Veracity and Valence. Examples of the most complex datasets include:
- Imaging (photos, X-rays, MRIs, and slides)
- Wave analysis such as EEG and ECG
- Audio files with associated transcripts
- Free text notes with natural language processing (NLP) outputs
- Mappings between structured concepts such as lab tests and the Logical Observation Identifiers Names and Codes (LOINC) codes or the International Classification of Diseases-9 (ICD9) and ICD10 codes
When machines think
— Pierre A Fournier (@pafournier) November 15, 2016
Cognitive computing has entered a new era and computers can now outperform humans at increasingly complex cognitive tasks. Add to that exponential computing power has increased the ease and speed of processing Big Data. IBM claims it is inching ahead of Google in the race for quantum computing power. It launched a website with tutorials on quantum computing and an interface to let outside programmers test its new chip.
Radiology and clinical data science
According to Keith J. Dreyer DO, PhD, nowhere is the opportunity for disruption “more apparent or imminent than at the crossroads of Radiology and the emerging field of Clinical Data Science.” Dreyer, who will be speaking at #RSNA16 on opening day, November 27, said, “To maintain our leadership position, as we enter the era of machine learning, it is essential that we serve our patients by directly managing the use of clinical data science towards the improvement of care—a position which will only strengthen our relevance in the care process as well as in future federal, commercial and accountable care discussions.”
Dreyer is vice chairman of Radiology Computer and Information Sciences at the recently opened Clinical Data Science Center at Massachusetts General Hospital. The center is exploring innovative ways to use cognitive computational algorithms such as machine learning and artificial neural networks in the diagnosis and treatment of disease.
Should doctors learn to code to help shape the algorithms of the future?
The gap between the supply and demand of data science skills is substantial and will only grow in the future. Healthcare will increasingly have a hard time competing for data science expertise with the financial and retail sectors. Knowing which problems to solve for clinical decision-making is the domain of the clinician. Should doctors learn to code?
In “Why Doctors of the Future May Know Code”, Dr. Michael Blum, associate vice chancellor for informatics, director of CDHI and professor of medicine at UCSF, says, “In medical school, physicians learn to use a stethoscope and to read x-rays to help identify what’s happening inside a patient’s body. Now we will add technologies including artificial intelligence and machine learning to our arsenal. We are eager to help develop these transformational tools that will help us more accurately and efficiently treat our patients.”
Doctors learning to code
In an era of electronic medical records and telemedicine, it’s handy to be technically proficient. So some doctors are teaching themselves to code. – Christina Farr, “Five Reasons Some Doctors are Learning to Code”
Pieter Kubben, MD, PhD, created a free online crash course for “Programming for Physicians”. He says, “Now, it is up to you to apply your medical knowledge and build new applications that will help both you and your colleagues all over the world in delivering better patient care!” Kubben says he is currently enrolled in the Big Data Specialization courses from Coursera. I found the introductory course in the six course series from Coursera listed at $59, but there is also a free course series available from Big Data University.
Doctors can play a beneficial part in shaping the future of clinical decision-making. Dr. Ziad Obermeyer, an assistant professor at Harvard Medical School, tells STAT news, “What we need to know more is, what are the rules the machine is learning, and how did it arrive at those rules? That’s sort of the next frontier of this.”
It’s all very well and good to say you’ve got an algorithm that’s good at predicting. Now let’s actually port them over to the real world in a safe and responsible and ethical way and see what happens. – Dr. Ziad Obermeyer
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