Five Ways Poor Data Quality Negatively Impacts Healthcare

Healthcare organizations rely on data to improve clinical, administrative, and financial outcomes. According to Morning Consult’s “Healthcare Data Readiness Crisis: Triage vs Transformation” report, 97% of healthcare leaders believe they have a data readiness challenge---and data readiness is a necessary precursor for digital transformation. And according to Sage Growth Partners’ market report, only 20% of healthcare organizations fully trust their data—this lack of trust is a brake on the acceleration of innovation and digital transformation.

Dirty data (or poor quality data) describes data that are inaccurate, outdated, redundant, incomplete, or formatted incorrectly. Some examples include duplicate patient records, diagnostic information missing from a patient record, outdated coding, inaccurate information about a patient’s allergy, and unstructured data. The accuracy and trustworthiness of this—and other information—is crucial toward the understanding of patients and their behavior, needs, and preferences, as well as improved whole-person care and outcomes. Such positive performance can only be achieved with holistic data collection that is properly aggregated, cleaned, semantically normalized, unified, and acted upon—by advanced analytics—to derive actionable insights.

Here are five ways that poor data quality negatively impacts healthcare:

1. Degraded patient care

   Health systems and hospitals need a complete picture of their patients to deliver high quality care and improved outcomes. A patient might visit multiple providers, undergo diagnostic and lab tests, fill medications from multiple pharmacies—disparate touch points in a patient journey, creating multiple data points. This data, though crucial, lacks technical capabilities to bring quality data together from clinical and non-clinical sources.

   Such limitation keeps providers from gaining an optimal 360-degree view of their patients and care population. The lack of patient data can mislead PCPs to deliver unnecessary care, delay in scheduling appointments, disconnected clinical workflows, out of network referrals, and many other gaps in the care journeys.

2. Ineffective decision making at point of care

   Data quality impacts communication between care teams, in terms of clinical decisions, care management, and patient outcomes. As patients should be at the center of care, efficient and accurate health data is foundational for better engagement and results in their care journeys.

   Let’s say a patient is seeking care from a health system where a physician refers a patient to a lab for a needed blood test. If the same PCP is unable to get information whether the scheduled test happened or not, the doctor might schedule another test—leading to delays in treatment and increasing care costs. Another instance where data impacts effective decision making are coding gaps to manage at-risk populations. HCC coding is not physician friendly, and high quality data better helps them keep a tab on RAF (Risk Adjustment Factor) scores—a necessity for managing risk within VBC.

   Effective data aggregation and activation, from clinical and non-clinical sources, helps improve data quality and drive powerful, decision-informing insights. These insights, especially at the point of care, improve physician decision making for improved care outcomes, as well as population health management..

3. Difficult to achieve interoperability

   Payers, providers, and patients—healthcare’s key stakeholders—all require high quality data for information that fuels experience and care journey success. This has been helped by the switch from paper to electronic health records, improving data interoperability to advance and grow.

   However, the biggest barrier to interoperability is getting clean and semantically normalized data. To unlock greater value, high quality data, often siloed and in multiple formats, must be standardized to ensure the information speaks the same language. This ultimately helps to create a unified, single patient record that can be trusted and shared through internal and external interoperability processes.

4. Non adherence with industry compliances

   Patients expect access to their health information with appropriate protection of their privacy. The access to right data not only keeps patients informed but also encourages engagement within their care decisions and health journeys.

   Patient data is regulated to mandate safeguard on the access, use and sharing of healthcare information. Without data quality controls in place, there is a higher risk of unauthorized and perhaps criminal behavior around PHI. The lack of validation across data pipelines overlook checkpoints such as data formats, security protocols etc. that are necessary to be compliant. Poor data quality and strategy prevent organizations from meeting new regulatory needs and result in high costs associated with audits and reporting.

5. Slower development of new treatments and medicines

   Life science companies require real-world evidence (RWE) gained from point of care interactions and clinical data, in order to improve success with drug development.

   Their clinical trials require RWE, backed by data, to effectively commercialize new medications into the marketplace. By utilizing a cohesive mix of historical, real-time and predictive analytics, it becomes possible to identify potential strengths and weaknesses in trials. However, this depends directly on the quality of data feed as poor quality data can lead to different conclusions during initial phases of drug (or treatment) development lifecycle. High quality data is necessary to lay a strong foundation for analytics which in turn opens doors to leverage a mix of data visualization techniques.

Better data for better healthcare

Healthcare data now constitutes more than 30% of the world's total data—and rising. Unfortunately, much of it is of lower quality, due to extensive siloing across systems and enterprises.Thesubstantial value and ability to be activated to improve performance and results, requires this data be of the highest and most trustworthy quality.

Poor quality data heavily contributes to degraded patient care, ineffective decision making at point of care, preventing interoperability, difficulties in adhering to compliances, and slowing development of new treatments and medicines. The challenge here is twofold: to access good quality data and combine data sets into a single, centralized source suitable for further analytics. Hence, we need better data for better healthcare.

In our next blog, we will discuss how to improve data quality. Stay tuned.