DataOps Can Help Alleviate Healthcare Organizations Data Ills
By Dan Potter, vice president of product marketing, Attunity, a division of Qlik.
Data is the lifeblood of every hospital and healthcare organization. Without it, doctors can’t access updated patient records for proper treatment; billing departments are unable to correctly process insurance claims; and research teams are limited in their ability to uncover new findings. Today there are issues with both data availability and access to the right information, for all users in a governed HIPAA compliant structure, that keeps healthcare organizations from effectively scaling the use of data to impact lives.
Data analytics is often discussed as a key element because of its potential to uncover insights that improve operations while also increasing care quality and efficiency. In today’s world of tight budgets and rising costs, its essential that organizations maximize staff time allocated to care and minimize costs. However, even if a hospital provides access to all its data, a lack of data literacy – an individual’s knowledge on how to use and analyze data – could limit data’s effectiveness towards improving care and operations.
Healthcare organizations must find a data cure that will address both data challenges: access to and use of information. The emerging methodology known as DataOps addresses both issues.
DataOps is a new approach to agile data integration that looks at the challenge from a holistic perspective of people, process and technology. It focuses on improved collaboration and automation of data flows across an organization. When done correctly, it results in an overall data set of processes that help the organization manage and use their data in real time to transform patience care and experience.
Fighting the Data Access Challenge
As the amount of data increases daily, one of the biggest issues is how to capture and manage it all efficiently. For healthcare this includes allowing appropriate real time access for all users to that data for analytics – while keeping it protected in accordance with HIPAA. One of the first steps is implementing modern data architectures that can handle the growing data volume. Open architectures based on hybrid and multi-cloud provide the greatest efficiency along with agility to improve patient care and increase operational efficiencies.
Data management platforms such as cloud data lakes and warehouses require continuous data integration so that the needs of real-time analytics are met. Change data capture (CDC) technology provides a non-invasive method to catch data and metadata changes from core application systems and databases, and stream them in real-time to the data management and analytics platforms. The beauty of CDC is that it works in conjunction with data lake and warehouse automaton to provide near real-time, analytics-ready data wherever it is required.
Healthcare organizations must track where data is located and how it is being used at all times to remain HIPAA compliant. A core component of a DataOps framework is the creation of an enterprise data catalog – an internal marketplace that lists what data is available for analytics. The marketplace informs clinicians, doctors and staff how the data was collected, shared and modified, along with the associated access rights. The catalog should also provide additional governance tasks transparently such as masking any Personally Identifiable Information (PII) to ensure regulatory compliance.
This type of organized collection, access and use of data is fundamental for DataOps frameworks. We are already seeing healthcare organizations put this structure in place before the official term of DataOps was used to describe data-based decision making. For example, Anne Arundel Medical Center relies heavily on building data-driven strategies to measure their progress towards business and clinical goals. The team pulls clinical and financial information together to have a full picture of patient care in a highly visualized format. As a result, the team can identify risks and then make the correct decision to meet clinical goals in a financially responsible manner.
Raising Your Organization’s Data Health
Data is of limited value to a healthcare provider if there is no understanding of its nature or potential application. Data must be analyzed regularly to uncover new insights and gaps. When access is made easier in a governed approach, individuals gain greater knowledge about the available information and how best to use it, helping to build their data literacy skills.
Additionally, when a new data set is created and shared in the data marketplace, it fosters greater collaboration across internal teams. A positive feedback loop on the data’s value and use is created while enhancing the individual and collective data literacy.
Healthcare organizations with greater data literacy are able to uncover valuable insights in time to truly transform patient care. In the case of Children’s Hospital of Pittsburgh of UPMC, integrated data analytics and visualized dashboards helped to improve registration workflow, reduced readmissions figures and the cost of patient care. Jefferson Health, operator of 14 hospitals, outpatient, urgent care, rehabilitation and imaging facilities in Pennsylvania and New Jersey, uses data analytics to uncover the delays in starting OR procedures and build greater efficiencies. As a result, Jefferson Health increased on-time OR starts by 25%, saving close to $300,000 a month and improving patient satisfaction. And Jefferson Health didn’t stop there – the organization is using data analytics to combat opioid addiction.
Taking your data to the next level should be a priority. Healthcare organizations must bring together the teams working and using data through the adoption of modern technologies. This is how DataOps was born, to fill in the gap and break the internal silos so all departments and executives can unlock the full potential of their data for improved patient care and experience.