Efficient health care is not only helping a person recover from illness but assisting with ongoing health management.
Each time a patient goes to the doctor, they trust the health care system with their most personal and private information.
With millions of visits generating new data daily, health-focused data management platforms and practices must continue evolving. Many hospitals already use data to enhance diagnoses and patient treatment, but the possibilities don't end there.
Customer experience has become a priority area for many industries, especially health care. This trend will continue, with data management tools such as artificial intelligence (AI) and machine learning (ML) improving patient experience for all people, even healthy ones.
People often limit good patient experience to people's interactions with health care providers when they are sick. But efficient health care is not only helping a person recover from illness but assisting with ongoing health management.
For example, over 66% of all U.S. adults use prescription drugs, but about half don't take them as prescribed. These patients may not be recovering from sickness but need the medicine to ensure their continued health and well-being. By pulling from previous data, such as when a prescription is set to run out, AI and ML can help by automatically ordering prescription drugs and sending reminders to take medicine.
Medicine is just one of the numerous ways data and AI will help with ongoing patient care. Creating personalized diet recommendations based on a patient's previous data, sending reminders about medical allergies and upcoming appointments, and tracking physical therapy are all improvements that are only possible when an efficient data management structure is in place.
The future of data in health care looks bright. Still, to fully achieve these outcomes, organizations need to establish a cloud-based data infrastructure, ensure cultural buy-in throughout their organization, and as an industry, increase the public's trust in how their data is used.
When it comes to data modernization, the first step for any industry is ensuring they have the data infrastructure needed to integrate more complex tools such as AI.
Physicians are required by law to safely store personal patient information for several years. This data often consists of large files such as high-resolution MRI scans, which means retrieving them takes a lot of bandwidth. As health care professionals implement AI, ML, and data warehouses into their management strategies, this process becomes streamlined, cutting down on costs.
One way to do this is by adopting the cloud as the organization's primary data infrastructure. Health care providers need solutions that can streamline data and ensure security, high-level performance, and long-term scalability. But transitioning to the cloud and optimizing cloud performance is easier said than done.
While increased telehealth practices have created initiatives to make this shift, they often stall or remain unfinished. Small practices do not have the required funding, and larger hospitals are often preoccupied with short-term cost saving and assisting current patients.
To assist with this transformation, the health care industry should recruit service providers to provide the necessary capabilities and expertise to make the move. This allows the health care industry to focus on what truly matters – patients' health – while industry provides the tools.
Once the move to the cloud is finished, hospitals can create more advanced data lakes to remove data silos, incorporate machine learning and make it easier to analyze diverse datasets while keeping data secure.
One challenge is the absence of funding and urgency for data modernization, partially due to a lack of cohesive discussion in the larger health care industry and government.
One way to address this is education. Educating decision-makers on the long-term benefits – both internal and external – of utilizing the cloud will lead to a broader emphasis and enhanced resources. Legislation, memos, and executive orders from the government will also help advance the understanding and acceptance of cloud modernization.
Finally, health care must address the public's distrust of allowing AI and other automations to handle their private data.
Putting in place essential security requirements, including the ability to audit all activity related to protected health information and PII and to encrypt PHI and PII at rest and in transit, is an important step to reassuring patients that their information will be safe. However data is used, keeping it confidential is always a top consideration.
When it comes to how data is used, the best way to address that concern is by implementing initial AI projects limited to specific areas. For example, a hospital may use AI to help diagnose one illness and expand in alignment with patient comfort levels as doctors and patients become more understanding and trusting of AI.
Data will continue to be a necessary tool for the health care industry moving forward. By embracing advanced data tools and best practices, patient care will continue to improve, creating a safer and healthier country.
Cloud Project Manager for ASCENDING, Gloria Zhang, draws on her diverse work experience spanning three continents to help health care clients of all sizes address industry pain points through data management and cloud migration. Gloria utilizes her background and passion for innovative cloud practices to guide leading hospitals and medical practices, including Kaiser Permanente, through major transformations that have revolutionized experiences for patients, physicians, and other medical professionals. Through scalable and user-friendly optimized cloud solutions, she serves industries such as health care, supply chain, finance, education and more. She also holds qualifications as an AWS Certified Solution Architect.