Bang Chau, Innovit

Embrace the Healthcare Data Transformation

By Bang Chau
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Bang Chau, Innovit

Best practices to achieve compliance as regulations evolve and customer requirements change.

The healthcare industry is in the midst of a dramatic supply chain transformation. As such, information systems need to capture the relevant data to support this change. The adoption of global data standards that aid traceability of devices and consumables across the entire value chain is now critical, from factory to hospital to patients.

In 2018, the industry will continue moving towards more accountable care, and suppliers are increasingly expected to take responsibility for the efficacy, safety and traceability of their products. Healthcare providers will be looking for ways to leverage master data to drive down procurement costs on contract purchases, new reimbursement models, improve care delivery, and in general, foster higher levels of patient safety. Medical device manufacturers and distributors will need to provide rich product data that is validated to much higher standards of quality, to their trading partners, such as the GPOs, hospitals, as well as regulatory agencies like the FDA, NHS and EU Commission.

There are a number of regulatory considerations and industry best practices that manufacturers and distributors of medical devices need to know as they work to meet changing government regulations as well as commercial customer requirements, such as those being driven by the Healthcare Transformation Group (HTG).

Data Quality Considerations

In the information age, data quality is a hotly debated topic. Having “trusted” and “reliable” master data is one of the foundational pillars for the digital healthcare transformation. One of the most complex issues confronting every participant in the healthcare value chain, from manufacturer to distributor to GPO to care provider and to the patient, is the issue of defining what “good quality data” is due to the fact that data quality is highly context specific.

Most people would agree that the desirable qualities of good data are accuracy, precision, consistency, timeliness, completeness, uniqueness and relevance. The question is, how do we measure these dimensions? What are the quantitative metrics we can use to define good data quality?

How to Address Master Data Challenges in Healthcare

Healthcare is a highly regulated industry, with participants in the industry relying on many systems that are purpose built for specific clinical, supply chain or regulatory applications. Accurate master data about medical products, equipment, care giver, patient and location are paramount to enabling these systems to function and integrate properly. As more and more paper-based processes are being digitized, the increasing silos of master data across these specialized systems have created enormous challenges around how data is maintained, validated, governed and shared across the organization between systems and consumers alike. The key to staying in control of your data amidst this explosion in systems and apps is to:

  • Establish formal, enterprise-wide master data management policy and practices. Obtain C-level support for these policies.
  • Assign clear ownership of data types and data elements to SMEs, and enforce this ownership and stewardship systematically. Relying on human enforcement and oversight is inconsistent and invariably fails over time.
  • Systemize these processes centrally—prevent silos of master data from growing without your visibility or control
  • Implement systems that are capable of strong workflow and task flow automation out of the box
  • Select systems that offer traceability and versioning of changes in your master data that are validated for software quality

Ultimately, if we are serious about minimizing harm to patients, and maintaining the “five rights”, the starting point is to have trusted and reliable data about the patient, the drug/device, the dosage/procedure, the route/location, and the time of application.

From a care provider’s perspective, this implies building best practices around:

  • Sourcing reliable drug/device data from manufacturers
  • Implementing proper product information management or master data management systems that assure data quality
  • Automating workflows that ensure data is reviewed and approved before it is released for consumption by clinical systems and clinicians

From a manufacturer’s perspective, this translates into sharing accurate product data to care providers, caregivers and channel partners in a timely manner. The most efficient way to achieve trusted data synchronization across your community is by adopting healthcare industry standards such as:

  • GS1, HBICC or ISBT for product identification
  • GDSN for synchronization of supply chain and commercial data
  • HL7/SPL for submitting UDI and CDER regulatory data

Once standards are in place, implementing repeatable processes that capture, maintain, validate, transform and publish product data to customers, channel partners and regulatory agencies will become a more manageable process.

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Bang Chau, Innovit

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