Whether we think about digital health as a patient or an information-centric experience, we have to wonder: how useful is it really? What are the advantages and disadvantages? We are looking at some important issues – such as interoperability of medical devices, patient data analytics, clinical decision support, and predictive analytics. This technology is a growing sector within digital health. Let’s see if these are useful for us.
Interoperability of medical devices
While there are many challenges to device interoperability in digital health, the FDA has made the case that it is essential to the patient’s care, reducing adverse events and errors. As a result, the agency has pushed for interoperability standards and the development of reliable, interoperable medical devices from different manufacturers. Critics, however, say that the FDA has not gone far enough. Here are some ways the agency is working to improve device interoperability.
First, interoperability standards and protocols are vital for the future of the medical industry. According to the West Health Institute, the lack of interoperability in medical devices costs the healthcare industry $30 billion annually. While purchase costs for medical devices and equipment are important, executives should consider the payoff. Ultimately, interoperability can improve patient care while reducing operating costs. However, progress will be slow until the entire medical industry commits to the goal.
Patient data analytics
Until recently, healthcare providers had little incentive to share patient data, making analytics difficult. Now, many providers are paid based on patient outcomes, so sharing data is easier and can save insurance companies money. One of the biggest challenges in using patient data for analytics is the lack of regulations, incentives, and systems for ensuring the privacy of patient data. In the US, the HIPAA privacy law aims to establish rules for patient control and privacy. However, the problem is that it creates significant privacy concerns, and it is unclear whether patients’ consent to use their data will be respected or violated. Also, because patient data is often de-identified, researchers can identify patients quickly.
Clinical decision support
Today’s healthcare environment involves numerous challenges, such as reducing clinical bottlenecks, maintaining patient safety, and avoiding costly hospital readmissions. It makes using insights from big data analytics and clinical decision support tools essential for fulfilling healthcare obligations. These tools process huge volumes of patient data and alert healthcare providers of potential issues before they arise. Therefore, they can improve the speed and accuracy of making medical decisions.
The main objective of clinical decision support is to help healthcare providers make decisions based on evidence-based evidence. The program module helps them identify and prioritize the best course of action based on data and medical knowledge. The first CDS system was developed at Stanford University in the 1970s. This program, known as MYCIN, used an artificial intelligence model to identify infectious diseases and recommend a course of treatment based on the patient’s symptoms and medical history. The machine even outperformed the medical staff in identifying the disease and suggesting the right treatment. However, the technology was never fully implemented because the concept of a computer acting as a medical expert was considered too far ahead.
As technological advancement increases, healthcare providers must utilize predictive analytics to improve patient care. By using analytics to analyze health data, providers can make informed decisions and ensure the safety of their patients. Moreover, these tools can predict future health outcomes, allowing physicians to tailor treatments to individual patients. For example, genomic data from cancer patients can be analyzed to determine the most effective treatment regimen. Because some diseases progress quickly, predictive analytics are vital in making these decisions.
For example, predictive analytics can help oncology infusion centers anticipate peak utilization times and adjust scheduling practices accordingly. By analyzing the utilization rates of infusion centers, a research team could determine when appointments are likely to be crowded and adjust scheduling practices accordingly. For example, popular midday appointments created unsustainable spikes that could not be sustained. However, predictive analytics can help keep appointment rates steady while dealing with sharp declines.
Effectiveness of digital therapeutics
As the demand for digital health technologies continues to increase, the development of digital therapeutics continues to grow at a rapid pace. To date, 25 therapeutics have been granted market approval worldwide. Another 23 commercially available solutions and at least 89 are in the pipeline, awaiting regulatory approval. Despite their early stage in development, digital therapeutics has several potential benefits for patients. As they do not interact directly with the human body, they may be safer than traditional medication therapy.
Digital therapeutics use artificial intelligence, machine learning, and natural language processing to evaluate real-world evidence and product performance data. The technology promises many advantages for all stakeholders: better-personalized care, easier access to healthcare in remote areas, and improved adherence to therapy. Healthcare providers can monitor patients in real-time, reducing the need for personal visits. They can also use the data generated to tailor treatments for individual patients.