Digital health startups are at the forefront of transforming the healthcare industry. Their solutions are enabling governments and other players to harness the power of technology to equalize access to quality medical care services.
There are several functions that digital health startups must focus on to succeed. These include patient engagement, data management, and interoperability.
Patients who are activated and engaged in their health care are more likely to experience better outcomes at lower costs. As a result, patient activation has become critical for new delivery and payment models like ACOs, patient-centered medical homes, and health insurance exchanges.
Patient engagement can include everything from self-assessment of symptoms to sharing data via wearables to a patient portal. Sometimes, patients also share decision-making with their physician in person.
The key for digital health startups is to offer tools that make it easy for patients to become engaged. This includes multi-channel communication, language tools, and automated appointment reminders.
In addition, it is essential to consider the healthcare industry’s current climate and regulatory environment when looking for potential digital health technologies to invest in. Finally, tFinally, these companies must prove their value through clinical trials to secure funding and grow.
Patient engagement products and solutions should be as straightforward and non-disruptive as possible, letting healthcare workers focus on their job duties. That means engagement tools should not take up time from other workflows or complicate clinical processes, explains Nabla.
Patient data management
Whether it’s medical, financial, or insurance data, digital health startups must manage all aspects of patient data. This includes collecting, storing, analyzing, and archiving it to protect patient privacy and security.
Patient data is essential because it provides healthcare organizations with valuable information that can be used to improve patient care. It can also help them predict disease, develop risk scores, and make better medical decisions.
In addition to patient data, digital health startups must also ensure that they comply with regulatory guidelines related to data privacy and security. These include the Health Insurance Portability and Accountability Act (HIPAA) and other United States government regulations.
A health startup can achieve this by providing secure cloud backups for all its applications and data. This can be vital for protecting against data breaches and other cyberattacks.
Another function of healthcare data management is archiving, which involves moving patient records into a centralized location that’s accessible and searchable. This helps to meet HA requirements and ensures that patients can find their files if they needneededrom ensuring that patients can access their health data; digital health startups should also strive to build patient engagement. This will help them increase patient retention and attract new patients faster. Ultimately, this will help their business grow and thrive.
Interoperability is the ability to exchange data between various systems seamlessly. It benefits healthcare organizations, including better patient outcomes and efficiency. It also allows public health agencies to share information more easily with other entities.
In the digital health space, startups are building technology solutions that can improve patient engagement and data management, but they must ensure that their products are interoperable to succeed. In addition, they need to be able to securely transfer data between their applications, labs, and hospitals without compromising patient privacy.
While the industry has seen a lot of uncertainty in the past few years, some startups are finding ways to survive and continue to deliver innovative solutions. For example, Medly and Elemy are still thriving despite being struck by a downturn in the healthcare sector.
To achieve this, some startups focus on a robust go-to-market strategy for their products aproductsices. Others are pioneering risk-based contracting, aggregator partnerships, and new marketplaces.
Another approach is to develop a single platform that connects different data sources from payers, pharma/biotechs, pharmacies, providers, and other stakeholders. This strategy can be an excellent way to establish a robust business model while providing a wide range of value propositions that span multiple use cases.
While this type of network approach can be challenging to execute, it can help startups build resilient and innovative businesses that can serve their customers in the long run. It is also an excellent way for startups to become a part of the broader health ecosystem, helping them build sustainable businesses.
Digital health startups are uniquely positioned to take advantage of data and use it to deliver business value. Whether they build products to improve patient engagement, optimize workflows, or reduce costs, they must rely on analytics to drive results.
A digital health startup needs a data store to ingest, prepare and manipulate information from many sources and applications. This includes data from electronic health records, web pages, mobile apps, and other platforms.
In addition, it’s essential to have a method of presenting and analyzing this data in a way that makes sense to non-data scientists. This requires using tools that allow users to visualize and explore data.
Ideally, an analytics platform lets people explore their data from a graphical interface and can help them find relationships between factors and trends. It also allows them to build models and deploy them in any way that suits their business needs.
The next step in the analytics process is to discovdiscovering data that can be used for specific objectives. This can be done through machine learning and other artificial intelligence capabilities that automate the process of modeling.
Once a model is built and deployed, it can be used to make decisions that will benefit the business. This is a crucial aspect of the analytics cycle, as it can take up to 80% of the time spent on an analytics project.