Electronically collected Patient Reported Outcomes (ePROs) can be a key way for healthcare providers, pharmaceutical companies or medical device companies to understand a large variety of measures such as symptoms, physical, social, emotional and other functioning of a patient.
Yet until recently, the vast majority of clinical care, clinical trials and post market surveillance of medications and medical devices have not included patient reported outcomes as a set of measures, or data points that assist with understanding how the patient, consumer or user of a device is coping beyond the “clinical outcome”.
The Internet of Things (IoT) is also starting to play an increasingly important role in patient monitoring - from sensors to wearable devices monitoring cardiac conditions or steps taken or flights climbed. However, as was discussed in a previous article Connected Health (cHealth) and gaining a full understanding of a patient situation is more than the connection and monitoring of wearable or other IoT devices.
In parallel with the now relatively new focus on PROs and the growth of IoT, “Big Data” has naturally taken on an increasing importance in making sense of the now significant amount of data being generated. In fact, the growth of data is expected to be exponential over the next several years
Companies that sell data analytic tools, dashboards and similar technologies have expressed that their technology is THE answer to understanding “Big Data”. The problem is this approach provides a retrospective view of the data – it is not real time, interactive and it does not or help the user with the interpretation of the data.
Other companies believe that they can assist healthcare organizations analyze and understand the data, in effect turning it into information by using vast quantities of data in the public or near public domain. This has met with mediocre acceptance, and one of the largest IT companies in the world is in effect re-examining their approach.
In examining and understanding why the above two approaches have not met expectations, we came to believe that there is an effective approach to understanding, in real time, vast volumes of data – and its impact on the care an individual patient may receive. This approach takes a specific use case, or set of use cases, and learns from real world outcomes how to understand and analyze the data. Using this approach, learning becomes part of an iterative process – learning by “machine” empowered and enhanced by the learning of humans.
At Willowglade, we believe that this approach will accelerate a better understanding of, for example, a specific clinical trial applicability for a patient - or what treatment might provide an optimal overall outcome for the patient, rather than simply an optimal clinical outcome.
Our platform not only has the capability to collect data from IoT devices, it has advanced Patient Reported Outcomes features and has the ability to incorporate data from EMRs - we can then leverage Machine Learning algorithms to understand for a specific circumstance how to probabilistically improve the patient quality of life as well as overall quality of care.
We started Willowglade with a vision of improving patient experiences and their interaction, communication and collaboration with the overall health system. With the introduction of Machine
Learning into our platform, we have taken the next step in improving the health of the individual patient by learning from the data of population of patients using our platform. Of course, this is done with consent from the patient.
To learn more about Willowglade Technologies, please feel free to visit our website.
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