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Healthcare Organizations are Driving Transformation With Data Certainty

Reliable, trustworthy data that is not only 100% accurate, but that can be quickly located, accessed, understood, and shared, is revolutionizing every aspect of healthcare.

The healthcare industry places better health at the forefront of everything it does. But whether your organization is a healthcare trust, hospital, part of the UK’s National Health Service, a private healthcare enterprise, research institute, insurance claim business, life sciences or pharmaceutical manufacturing organization, change is inevitable.

Effectively managing data quality in the face of change is vital in the healthcare sector. Electronic healthcare records and reports are not only governed by strict regulations but also affect physical treatments and policies. They have a very real and tangible impact on people’s lives.

You can rely on IDS' data certainty experts, and our breadth of healthcare industry expertise, to help you navigate the challenges of today's shifting environment: 

  • Ever increasing data volumes and complexity
  • The requirement to keep ever-expanding data sets secure
  • Cost reduction through process improvement and system integration
  • Data compliance, governance and privacy
  • ERP migration from legacy to cloud
  • Documenting metrics for quality of care and generating insights
  • Patient portals and patient information access
  • Interoperability of electronic health records (EHRs)
  • Industry consolidation through mergers and acquisitions.
IDS | Take Back Control of Your Data

How IDS Can Help

IDS understands the intense pressures that healthcare organizations are under to modernize and innovate, while simultaneously reducing costs and becoming more efficient. Transform healthcare with IDS’s end-to-end data certainty solution, Kovenant™, and automate data management to assure 100% of the data, through 100% of the journey, all the time.


The Impact of Digital Transformation in Healthcare

Digital transformation is having a broad and meaningful impact on some of the healthcare industry’s most persistent challenges, especially in the privatized healthcare sectors in the UK and USA.

Access to personal health information empowers patients to make informed treatment decisions. Expectations are higher too. Healthcare providers must respond faster and dispense high-quality care within a highly regulated, complex and risk-averse sector.

At the same time, healthcare providers need to adjust business and care delivery models to improve care outcomes while ensuring safety, efficacy and affordability for their patients.


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Three Challenges Caused by Poor Data Quality in Healthcare

Maintaining strict data quality is required to avoid problems throughout a healthcare organization. This impact includes everything from policy-level decision-making to patient care and supplier management.

1. Patient frustration and mistreatment
When source data has been improperly entered into an electronic healthcare system, manual processing is typically required to resolve discrepancies and/or inaccuracies. This can lead to delays and even mistreatments, both of which result in a poor experience for patients. Automating the manual processes using the iData toolkit can save more than 50% in time and costs, as well as assuring 100% of the data.

2. Decrease in efficiency and increase in bottlenecks
With manual interventions to clean incorrect data regularly slowing or stopping operations on otherwise automated jobs, employee efficiency is dramatically affected.

This can lead to inconsistent data management and administrative back-office procedures, and backlogs in work that could have been avoided with more systematic approaches.

3. Poor and ineffective policy decisions
Stakeholders in healthcare organizations are increasingly reliant on ever expanding datasets to make smarter and more informed decisions. In a healthcare organization with poor data quality, and given that aggregated, augmented and transformed datasets are required to drive informed decision-making, even small inaccuracies can compound when impacting multiple transformations or in relation to mergers and acquisitions.

As these problems continue to occur at a large number of healthcare organizations that have failed to modernize their systems and processes, decision-makers must move fast to begin developing and procuring better systems to handle these cases. 



Automating Data Obfuscation to Eliminate Risk and Cost

Deliver an Exceptional Patient Experience

Duplicate patient data and inaccurate records cause huge challenges for healthcare organizations as they seek to provide excellent care and mitigate risk.

To meet rising patient expectations for engaging, consistent experiences and always-on accessibility, healthcare organizations need to build a single point of truth for patient, employee, financial and provider data so they can deepen high-value relationships between providers and patients.

These experiences must be relevant for all healthcare consumersfrom young to oldto drive up their perception of the organization and net promoter score (NPS) as an indicator of the organization’s ability to access and analyze accurate data, augment this and drive an ever-improving patient experience.

With insights from your single source of truth, you can identify potential areas of risk, proactively manage patient care, and deliver great customer experience.


Data Privacy and Protection is Vital

IDS assists healthcare organizations with solutions that obfuscate or synthesize 100% of your sensitive patient data to support test data management in the healthcare industry at scale.

Our Kovenant™ methodology and full data quality, transformation and testing toolkit, iData, seamlessly automates manual processes to enable comprehensive and secure data transformation in the healthcare sector.

Transform Healthcare Data Management with the Kovenant™ Methodology 

To deliver value and consistent, scalable results from data-driven digital transformation initiatives, healthcare IT and data professionals must architect and deploy an intelligent data management foundation.

By refocusing from a single transformation or data quality bottleneck, to look at data quality end-to-end, the organization is able to transform both the quality of the data, and also the roles and tasks of data professionals, so that instead of focusing on manual testing, they can refocus on value-added activities related to insight, performance management and business improvement, for example.