Blog Post

Healthcare and Technology Challenges Regarding Data Integrity, Collection, and Reporting

Data Integrity Web

Healthcare is at a crossroads with regard to data, analytics, and reporting. While healthcare has historically lagged other industries in its approach to data, there is growing recognition that it is necessary to gather, analyze, and apply data in a meaningful way to facilitate the provision of high-quality patient care while supporting safety, billing, and reporting needs. While all industries face many similar challenges in their approach to data management and reporting, healthcare organizations encounter several additional unique hurdles.

(A Lack of) Data Governance

As healthcare providers struggle to manage the acquisition and application of rapidly growing data sets, the need for robust data governance processes is greater than ever before.

Data governance—essentially an organization-wide strategy and framework for the collection, standardization, and curation of data to ensure a single source of truth—is a relatively new concept in healthcare. However, as early as the late 1960s, some organizations used systems to capture and store electronic information for certain patient demographic, lab, and billing data. But these were stand-alone, unintegrated systems—EHR islands surrounded by a sea of patient data in handwritten notes in patient charts that were often illegible.

This changed to a large degree following the 2009 passage of the ARRA/HITECH Act, the gist of which was that providers accepting Medicare/Medicaid patients were required to demonstrate “meaningful use” of a certified EHR. Extra money was paid to providers certifying as early as 2011, and providers unable to certify by 2015 started to be progressively penalized every year through reductions in reimbursements.

Predictably, practices that previously used paper implemented EHRs, and hospitals increasingly turned to enterprise EHR vendors that could work in most departments while providing a single patient record. From 2008 to 2015, the ONC found that basic EHR usage in hospitals soared from 9.4% in 2008 to 83.8% in 2015, and the degree of adoption among office-based physicians was almost as dramatic.

Unfortunately, enterprise data governance practices have not been adopted as quickly. In a recent survey of 104 healthcare organizations, 56% reported incomplete or nonexistent enterprise-wide governance processes. Ultimately, the root cause of a lack of a data governance program is leadership apathy. Without an imperative from leadership, the following barriers will prevent any organization from developing and implementing successful data governance:

  • Systems Fragmentation: Healthcare organizations have always struggled with data fragmentation. Over time, as the availability of electronic systems expanded, they were increasingly adopted by different departments. Often, each department pursued a “best of breed” strategy (i.e., picking the system with great functionality for that department with little regard for what would be best for the organization as a whole). The typical outcome was a number of fragmented, poorly integrated systems requiring a large amount of dual data entry and toggling between systems, which led to inaccuracies and errors. A recent analysis found that an average hospital system has 16 different EHR vendors in use at affiliated physician practices, and only 2% of hospitals have a single vendor. Furthermore, given the disruption and expense of implementing an enterprise EHR, many organizations will have to be satisfied with their status quo for some time.
  • Lack of Standardization: Most departments prefer to do things “our way,” which includes how they define and capture data elements. Combining this data results in challenges when various taxonomies, terminologies, and/or coding standards are used across clinicians, departments, and/or locations. This problem is further exacerbated by the use of disparate EHRs, which often treat each clinical data element differently.
  • Lack of Resources: Whether because of competing projects or the perceived lack of ROI, data governance initiatives are typically low on the priority list and often do not have a strong commitment from leadership. Also, the ability to secure funding has become even more difficult as healthcare organizations nationwide are experiencing increased costs of doing business, while reimbursements for care continue to decline.
  • Lack of Ownership: To ensure that data governance is properly applied, resourced, and enforced within the organization, ownership must be at the highest level—by a governance committee composed of senior individuals across departments and functions. Unfortunately, this requires time and focus from busy individuals who often view data governance as less than exciting. Data governance efforts led at lower levels are unusually unable to attract the necessary resources, as well as enforce the organizational standardization that is required to ensure a successful data governance program.

Data Integrity

Data integrity, a component of data governance, involves the accuracy, completeness, and trustworthiness of the health record. While certain elements of data integrity, such as authorship validation and auditing, are common across all industries, healthcare organizations face unique challenges here as well:

  • Physician Documentation: Much has been written about physicians’ relationships with EHRs. Often heralded as ensuring quality and patient safety while promoting efficiency, EHRs, in the opinion of physicians, have turned them into glorified data entry specialists. And with meaningful use and ICD-10, and due to other regulatory and reporting needs, physicians’ tasks have increasingly included box checking and other documentation requirements that do not directly impact, and may even distract from, the care given to patients. EHR vendors have introduced several ease-of-use features that routinely jeopardize the integrity of the medical record, including the following:
    • Copy and paste features enable physicians to replicate a favorite note from a patient’s previous encounter, or even from a different patient’s chart, but they may not adjust all variations between the copied and the current visit. This can lead to inaccuracies by introducing incorrect or even contradictory information, which may put patient safety at risk. Additionally, the sheer amount of copying almost guarantees errors; one recent study of 23,630 provider notes found that 46% of text was copied from elsewhere.
    • Drop-down menus may limit the provider’s ability to accurately document the patient’s condition because the option that is most similar to the patient’s situation must be selected.Documentation templates are typically created to streamline workflows while maximizing reimbursement by ensuring that all relevant data has been collected. However, a template may not exist for a specific visit type or medical problem, resulting in a note that does not accurately reflect the condition of the patient.
  • Patient Matching: Duplicate records can occur due to various spellings of a name, a lack of standardization across systems or processes, data entry errors, or an event that changes patient demographics between visits (e.g., marriage or divorce, change of address). Industry estimates regarding duplicate medical records vary, but generally fall between 5% and 12% for a typical hospital. The challenge of matching patient records increases across organizations because of the increased variations in data sources, data format standards, and data governance processes.

Collection of External Data

While healthcare organizations are wrestling with these challenges of collecting and combining data internally, the current data boom has also exponentially increased the quantity and type of external patient data that is available. Organizations are now able to get data from other practices and hospitals, SNFs and home health agencies, behavioral health providers, and external labs and pharmacies. Data is even available from the patients themselves, through direct entry via a portal or a mobile app, from connected at-home devices such as a Bluetooth-enabled scale or patient wearables like a Fitbit or Apple watch. The growth of the wearables market, and the potential amount of data it will produce, is staggering and will only continue to increase; in 2017, manufacturers shipped 115.4 million units, up from 104.6 million in 2016.

The primary barriers to the collection of external data are the overall lack of interoperability in an industry with proprietary vendor standards, systems geared more toward billing and reporting, and a historic lack of a business case to share data. As these barriers are overcome, all this information must be presented to providers in an organized and meaningful manner, and they, in turn, must learn how to incorporate it into their clinical decision-making.


In meeting their reporting requirements, all healthcare entities face challenges, some of which may be a predictable downstream result of the organization’s data governance, data integrity, and data collection practices and failures that lead to the need for costly manual data extraction processes. Simply put: Bad data in, bad data out.

Fortunately, most of these challenges can be overcome with a thoughtful and committed approach to data governance. The EHR market is consolidating, and vendors are enhancing interoperability. Patient-matching algorithms and processes are improving. Finally, organizations are increasingly recognizing the value of their data as a true asset, and the industry is becoming more analytically driven to meet the Triple Aim: improved patient experience of care, better health of populations, and lower costs.