The Benefits of an Algorithmic Approach to Medicine
The purpose of a medical algorithm is to improve the delivery of medical care by removing some of the uncertainty from medical decision-making. These algorithms are able to predict and diagnose diseases with extreme accuracy. They are also able to predict related factors such as the likelihood of death, the length of hospital stays, and the chance of hospital readmission.
By providing a predetermined sequence of steps for treating patients, combined with real time and medical records data, electronic platforms are able to eliminate differences in treatment approaches between clinicians. By having experts in the healthcare sector for which the algorithm is written provide support during the development process, top-tier medical care can be given consistently around the world, even in countries with less established healthcare systems.
Medical algorithms used in the healthcare industry include:
Algorithms that run in the cloud, aggregating large amounts of data including electronic medical records data, past patient history, streaming data from monitors and sensor near or attached to the patient.
Algorithms that run on the gateway or local servers.
Algorithms that run on the device itself, using locally collected data, and possibly data from other local or remote sources.
There are two main classifications of algorithms:
Algorithms that present an already known diagnosis or parameters to the health care provider, where the provider is familiar with the output i.e. Blood Pressure, Pulse OX, End Tidal CO2, Sepsis detected etc.
Algorithms that present some type of new indication, by interpreting the data and presenting an indication, diagnosis or an “index” of a possible underlying condition or conditions, that the health care provider may not have seen prior to using the system.
Medical algorithm advancements have also allowed for more complex devices to take the stage in day-to-day medical practice. For instance, data compression algorithms, paired with more advanced processors, has allowed for the use of MRI and ultrasound machines. The amount of data present in a 3D body scan is too great to be processed and sent to a computer without compressing the data into constituent components and unwrapping the data at the receiving end.
With modern technological improvements, there has been a surge of possibilities for medical algorithm advancements. One of the major focal areas is artificial intelligence (AI). With the ability to store massive amounts of data, computers are able to “learn” from the wealth of knowledge in medical databases. With the help of a clinician, machines can be told which biological signals or images contain certain medical conditions, or not. With this, machines are able to “remember” what certain diseases look like and can diagnose patients faster and sometimes, more accurately than a clinician due to the profound memory a machine has compared to the human brain.
AI has been utilized in medicine to classify aberrant ECG arrhythmias in cardiology, classify tumors as benign or malignant in oncology, detect ectopic pregnancies in gynecology, find joint centers in orthopedics, and even search clinical databases to perform large-scale medical data analytics. Data analytics algorithms are then able to find bottlenecks in healthcare to inspire new medical algorithms to be invented. As more of these algorithms are developed, future, smarter algorithms have higher potential of reaching the market, due to the scientific community's ability to build on the shoulders of preceding medical algorithms.
As medical technology continues to progress each year, it is the hope of engineers and physicians alike to reduce the 100,000 annual deaths due to medical error to zero – eradicating medical errors entirely. Achieving this goal will require a joint effort from healthcare professionals across numerous disciplines.