Data challenges of healthcare organizations

To provide optimal care for patients and guidance that helps patients make good healthcare decisions, healthcare workers need high-quality, clinical data about their patients. Delivering the right data, in the right format, to the right person at the right time, is challenging for health IT, especially given the ethical and regulatory requirements for health-data handling. In addition, medical innovations are constantly increasing the amount and complexity of healthcare data. According to RBC Capital Markets, 30 percent of the world's data was being generated by healthcare in 2018. By 2025, healthcare data will grow annually by 36 percent. Traditional health data-processing strategies struggle to support this rapid increase in data volume and complexity.

Many healthcare organizations are improving patient outcomes by using population health analytics. Organizations are also using precision medicine, which is defined as "an innovative approach that considers individual differences in patients' genes, environments, and lifestyles." Precision medicine is increasing healthcare effectiveness, but it's also creating novel data-processing challenges for healthcare organizations. Standard precision-medicine approaches are also difficult to scale beyond the one-patient-at-a-time paradigm. Healthcare organizations must reduce the time from acquiring raw data to delivering usable information to frontline workers. That information must be accurate, and it must be presented in a form that clinicians can easily access, understand, and apply.

Healthcare data is irreplaceable and is a highly valuable asset of many healthcare organizations. Therefore, you must treat healthcare data as an asset. Your healthcare organization must earn patient trust and manage reputational risk by collecting and honoring patient consent and protecting data from improper access and use. Your healthcare organization must simultaneously protect patient privacy, comply with rigorous, diverse regulatory constraints, and provide high-quality data quickly to healthcare workers, collaborators, and patients. You must also decide whether you can safely monetize healthcare data in a way that is consistent with your mission, your data security and privacy policies, and patient consent. Challenges include the following:

• Traditional healthcare data pipelines are being overwhelmed because they were not built to handle these progressively more rigorous and challenging requirements.

• Traditional systems are typically siloed. To provide a comprehensive view of the relevant data and the individual patient, modern systems must be integrated and interoperable.

• Traditional systems are often organized around a single data modality. Modern systems must be inherently multimodal.

• Traditional systems were not designed to handle data at the scale and velocity required of modern systems.

• Traditional systems are typically designed to run on premises and are optimized for available IT resources. Modern systems must be able to take advantage of data storage and processing resources in hybrid on-premises–cloud environments and sometimes multicloud environments.

Healthcare organizations that adopt and run on a modern health data strategy position themselves to advance as innovation accelerates in healthcare and life sciences.