Every day, hospitals and doctors collect infinite amounts of data on individual patients, diseases and medical procedures. The rise of a new generation of robotic, precision, and surgical devices has created vast stores of video, imaging, and other electronic surgical notes, while the proliferation of digital patient technologies, such as electronic medical records, wearable sensors and mobile apps are generating a menu of personalized data on each patient.
There are more than 500,000 medical technologies currently available, according to a 2018 Deloitte medical device report.1 A connected medical device is one which is able to generate, collect, analyze or transmit data or images, and can connect to health care provider networks and transmit data to either a cloud repository or internal servers in order to prevent, diagnose or treat diseases. The questions that remain are: How do all these devices and technologies talk to one another? How do we make sense of the available data to enhance surgical planning and safety and improve patient outcomes?
“Interoperability is the biggest device challenge today, with the hurdles of data exchange at its core. The answer lies in a connected ecosystem that unifies imaging and medical devices, supported by advanced AI analytics and a vast repository of images, hospital data (EMR, PACs, RIS) and other clinical data. Many hospitals, doctors, and surgeons are pushing towards this integrative experience for patients,” said Rama Kondru, Chief Technology Officer of Acorn AI, a Medidata company, which aims to integrate the new generation of medical devices and digital patient technologies and to provide the derived data to doctors and patients with its platform. “We are a means to an end,” Kondru said.
The power of a connected device ecosystem is in its ability to collect and crunch available patient and medical data to enhance and personalize surgical outcomes and elevate preoperative planning, inter-operative decision making, physician education and patient safety and recovery. AI analysis of images can be used to stratify disease phenotypes and aid radiologists in diagnosis of a disease.
All of the information that is collected on the patient from various sources floods into the platform ecosystem and is integrated into a seamless stream in the cloud, which is then made accessible via an intuitive portal to both doctors and patients. Doctors can then use existing artificial intelligence algorithms to improve outcomes.
For an example of how a connected device ecosystem might work in the real world, imagine a patient who is slated for robotic surgery. The platform would first access patient level information already stored in the electronic medical record from doctor visits, wearables, and digital apps. Then it would determine the ideal steps for pre-operative, during surgery and post- operative planning for best outcomes. The platform could also recommend the ideal surgical instruments to use for the patient’s particular procedure.
The platform would even offer benefits post-surgery. Let’s say if a surgical implant had sensors embedded into it, which were linked into the platform. These sensors communicate information to a series of digital apps that could provide diagnostics and insights for personalized outcomes. The patient’s post-surgical treatment plan could also be informed by co-morbidities and potential drug-interactions collected via the patient’s electronic medical record.
AcornAI’s network ecosystem is built to leverage a robust universe of data: it already has EMR connections to over 800 hospitals and processes over 400 million images a year, adding to an existing collection of 1.5 billion medical images. More is being fed into the network every day, because improved patient outcomes depends on improved patient data connectivity.
1 Deloitte Centre for Health Solutions. Medtech and the Internet of Medical Things: How connected medical devices are transforming healthcare; https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/gx-lshc-medtech-iomt-brochure.pdf (Accessed July 2019)
Sponsored content by Medidata Solutions