Making quality data accessible and meaningful is key to improving many areas of healthcare organizations
Digital innovation is providing healthcare and life sciences organizations with powerful, AI-driven tools to overcome many of the most pressing challenges facing the industry today. Leading the list of these challenges is how to collect, store and manage vast amounts of data from multiple sources—and how to mine that data for the actionable insights needed to set policy, run healthcare facilities efficiently and provide the best possible patient care.
Automated healthcare solutions—driven by sophisticated machine learning—are being combined with cloud-based platforms to give healthcare organizations all kinds of new ways to leverage the flood of data made available in an endlessly connected world.
From conducting research on new medications and therapies, to scheduling and billing a patient’s routine doctor visit—it’s data that drives decisions and defines an organization’s goals. But traditional models of managing data for research, development and patient care lack the resources and tools to make the best use of globally accessible data from sources of all kinds. Automated healthcare solutions powered by advanced machine learning can create a framework, not only for making data accessible, but for making it meaningful.
Automated data management accelerates research and development
According to a recent MedicineNet report, it takes an average of 12 years for a new drug to complete all phases of clinical testing and receive FDA approval. Difficulties managing the large and varied sets of patient data have a significant impact on the progress of medications toward approval and marketing.
Researchers now have access to more information about clinical trial participants than ever before. Along with standard health records, study runners can draw from databases held by a variety of third-party entities, such as insurance companies. However, these databases may not match in terms of content or formatting, and the volume of raw data must still be processed in order to extract useful insights.
A comprehensive healthcare automation solution can store and process large sets of records and mine them for relevant information to meet different organizational goals. Easier access to necessary data can streamline the research process and provide important insights for organizational decisions.
With the support of robust cloud-based platforms, automated data management systems allow researchers to share study results and collaborate in real-time with teams from around the world. These systems also allow research organizations to identify and access a wide range of patient data from a worldwide population and to interact with a larger pool of study participants through digital tools for collecting data and reporting outcomes.
Automated systems streamline clinical management
Automation can help entities in all areas of healthcare operate more efficiently and economically. In hospitals, clinics and other patient-facing organizations, the volume of patient data can become overwhelming and cause delays in providing care when relevant personnel is unable to access needed patient information.
Automation can organize and track patient data from multiple sources and make it available on-demand through cloud-based technologies. Likewise, automating general record keeping and accounting functions can save time and free staff for other tasks. An integrated digital system can also streamline billing processes and manage scheduling to reduce losses due to nonpayment, broken appointments and failed follow-ups.
Automated data management offers actionable insights
AI-powered automation offers the tools for processing data in order to gain different insights and perspectives. With the sophisticated algorithms of advanced machine learning, a given set of patient data could be mined to identify trends.
The same data could be used to help a medical or life sciences institution make decisions about how to allocate research funds or to find a new direction for the company. It can deliver insights in formats appropriate for different types of decision-makers in an organization, such as administrators or clinical staff.
Correcting regulatory noncompliance issues can delay the progress of medications and treatments toward approval and marketing, but automation can help to streamline regulatory processes. Automated data management can quickly review all available patient and clinical trial data in order to identify any areas of noncompliance—a slow and time-consuming task when done with legacy systems.
The challenge of managing large data sets more efficiently and economically affects institutions and organizations in all areas of healthcare and life sciences. But innovative, AI-powered healthcare automation solutions and cloud-based technologies can help researchers, healthcare providers and administrators gain the insights they need to meet organizational goals and provide quality care.
YOUR NEXT READ:
CUSTOMER SUCCESS STORY
Mayo Clinic saved 40,000 hours of manual work, reduced risk and increased data integrity with Catalytic.