富特旺根大学使用 PTC 开发“IoT Walker”和“IoT Wheelchair”

The primary challenge was to monitor the progress of the plans adopted by the patients. In most cases, the patient will start with a plan and give up in the middle. It is important to motivate them to continue through the plan.

Every day, vast quantities of data are collected about patients as they pass through health service organizations—from operational data such as treatment history and medications to physiological data captured by medical devices. The insights hidden within this treasure trove of data can be used to support more personalized treatments, more accurate diagnosis and more advanced preparative care. But since the information is generated faster than most organizations can consume it, unlocking the power of this big data can be a struggle. This type of predictive approach not only improves patient care—it also helps to reduce costs, because in the healthcare industry, prevention is almost always more cost-effective than treatment. However, collecting, analyzing and presenting these data-streams in a way that clinicians can easily understand can pose a significant technical challenge.

Today's healthcare providers face a daunting challenge: sustaining or improving patient care levels despite shortages of clinicians and nurses, increasing competition, and financial pressure.

With a need to deliver round-the-clock reliability and peace of mind to seniors and their loved ones should the unfortunate happen, it is vital for Jupl to maximize uptime.

The client faced challenges in their attempt to improve patient care through objective data gathered from smart products such as Apple Watch, Fitbit, Carbon, and Samsung Gear which relates to patient compliance with prescribed medication regimen and their lifestyle patterns. The client was searching for an appropriate analytics algorithm which would help them gain actionable insights from the unstructured data. The backend came with a hive table which helped import data to the Hadoop environment. The company faced roadblocks in extracting behavioural context over time to tailor treatment according to the needs of patients with Serious Mental Illnesses (SMI). They aimed at developing a passive sensing mobile application which would enable full control and surveillance of the patients.

Mevia provides intelligent solutions to improve the quality of medical treatments. Mevia develops solutions that enable people to take the right medication at the right time. This helps improve adherence, which is one of the greatest obstacles to successful medical treatments. According to statistics from the IMS Health Institute poor adherence costs over $500bn globally in avoidable healthcare costs and the World Health Organisation said that, “improving adherence would have a greater impact on health than any potential discovery in medicine.” Mevia has created technology, which can be adapted to work in blister packs, pill dispensers, and pill pouches, providing patient support and personal adherence feedback in real-time. Missed medication and adherence is a huge challenge globally for healthcare providers and pharmaceutical companies alike. Up to $289bn is spent on missed medication and adherence issues in the US, and the global pharmaceutical market’s revenue loss is estimated to be $564 billion due to medication non adherence. Being an innovative, yet growing company, Mevia needed to partner with an expert in IoT. However, this partner needed to offer competitive pricing, global coverage, and the agility to adapt their services as Mevia grows. Many providers had high fixed costs which would prove to be a deal breaker for Mevia.