Connected healthcare refers to a model of healthcare management that uses available consumer technologies to deliver patient care outside a normal healthcare setting. This involves integrating data collected by medical devices in healthcare facilities and outside their walls into a centralized electronic health record (EHR).
Some common examples of connected healthcare include the remote monitoring of chronically ill patients with a combination of fitness trackers and mobile apps, and telehealth programs involving remote consultations by way of video conferencing technology. Here we’ll briefly illustrate several ways connected healthcare reduces the costs involved with providing quality healthcare, and highlight some of the benefits that patients experience.
Uniform time stamping across devices
It seems rudimentary but it’s a major point of contention in many facilities: what is the correct time? Staff might record time according to the wall clock closest to them; others may the time shown on the Windows desktop PC in the exam room, while the doctor uses his wrist watch. Accurate timestamps are critical in the medical fields, and caregivers using several different devices to administer medicine must work from the same clock to ensure the highest quality of care. For this, devices must synchronize to the facility datacenter using Network Time Protocol (NTP). IT departments might use Windows Server 2019 or another server software to authenticate and synchronize any EHR timestamping by medical devices that connect to the domain.
Automating data exchanges
Reducing the data entry role for medical staff is advantageous for two reasons. It means they can focus more time on caregiving, and it lowers the risk of costly human error that might occur when manually transferring data from a medical device onto another computer or mobile endpoint. In a connected healthcare setting, medical devices for collecting health readings perform data exchanges as independently as possible with the EHR and other endpoints that access it. One challenge for healthcare IT is that the same device has different requirements from department to department— for example, a barcode scanner and imager is used differently in oncology than it would be in cardiac care. To automate workflows in these scenarios, IT would use OEM software development kits to optimize data collection appropriately depending on which team is using it.
Tracking patient vitals anywhere
Fitness trackers like Fitbit, Apple Watch, and Samsung Galaxy Watch are ubiquitous on the consumer market, and highly useful in healthcare. There are even clinical grade wearables utilized in healthcare for patients suffering from chronic illnesses such as cancer, Parkinson’s Disease, and pulmonary disease. The devices help medical practitioners measure a wide array of behaviors and biological signs that are relevant to treatment — exposure to the sun, gut and respiratory activity, prenatal contraction monitoring, and bioelectrical activity that is telling in emotional assessments and fitness levels, among other measures. The data allows doctors to drill down and offer a more precise level of care.
Improving R&D for medicines
Biopharma companies can leverage data collected by wearables, sensor patches, and other connected medical devices to improve the performance of products. Tapping a large compilation of patient health data, companies increase the efficacy of medications, give medical practitioners insight into appropriate treatment before it is prescribed, and track patient outcomes after the medicine is administered.
Traditionally, clinical trial data comes from clinical appointments and diaries. The connected health apps and technologies lifts the burden for patients who no longer have to physically log readings and go to appointments in person. No longer bound by proximity limitations of the clinical trial, biopharma firms can track the biometrics of participants around the world. They are able to monitor safety issues and analyze results in real-time, and the connected devices reduce the data entry workload, margin of error, and the time and resources needed to complete the trial.
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