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Enterprise Health Information Management And Data Governance

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What Is Data Governance In Healthcare

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Data governance is an approach to managing data that allows organizations to balance two needs: the need to collect and secure information while also getting value from that information. But its much more than that. Health data consists of patients personal and health information as well as financial data. If managed and used appropriately, this information can be the most important asset a facility owns.

According To Gartner One

In this research report from Gartner, learn how data and analytics leaders can adopt an adaptive governance model that helps them succeed in their digital business initiatives.

Gartner “Adaptive Data and Analytics Governance to Achieve Digital Business Success”, Saul Judah, Remi Gulzar, September 23, 2020

Gartner is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and is used herein with permission. All rights reserved.

Enable Early Adopters To Become Enterprise Data Governance Leaders And Mentors

As the first couple of teams achieve their data governance objectives, it may be time for them to generalize their processes for broader adoption. This shift in perspective from local to organizational naturally coincides with each early adopters transition from team leader to enterprise data governance leader. With new success under their belts, they are well-positioned to champion data governance generally and to recruit and mentor others their message is not just conceptualits based in their own hard-won experience, which gives them instant credibility and a host of concrete examples to drive home the how and why of data governance.

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Paperback 400 Pages Published 2015

ISBN-10: 1-58426-155-2 / 1584261552ISBN-13: 978-1-58426-155-1 / 9781584261551 Enterprise Health Information Management and Data Governance provides the fundamentals, principles, … more » and practices for managing the data asset. Data growth rates are increasing at a phenomenal pace for most businesses. Enterprise Health Information Management and Data Governance tackles how healthcare organizations can manage their data in this era of dramatic data explosion and growing deployment of information technologies. Healthcare organizations must understand that their sustainability and future viability relies on the quality of their data and how they manage this resource on an enterprise-wide basis. This text provides a framework and logical structure to help students understand the components of health information management in a digital era and to provide them with opportunities to develop the necessary skills for performing functions associated with these components.Provides an outline of enterprise-wide information management for healthcare Each chapter incorporates a case study, providing a real-world perspective to student learning and exploring the concepts and interrelationships of EIM and data governance Students are invited to be a part of the case study through a set of activities at the end of each chapter Advanced concepts are provided at the end of each chapter for in-depth analysis and study Aligns with AHIMA core competencies « less

Hims Role In Data Governance

Enterprise Health Information Management and Data Governance by Merida ...

Professionals leading data governance initiatives within an organization must be able to:

  • Develop policies and procedures that support data governance efforts.
  • Educate all members of the organization about the importance of data governance and how it relates to their roles.
  • Leverage clinical, financial, and administrative data to support key organizational initiatives.
  • Measure the return on investment on information governance initiatives.

Those working in data governance in healthcare must also be able to bridge gaps between departments so that everyone works together toward the common goal of making information complete, accurate, reliable, available, interoperable, and secure. Skills such as project management, leadership, and team building are critical.

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Identify The Organizational Priorities

For analysts, report writers, and other data practitioners, its often easy to identify the data problems. But deciding where to focus data governance based on data awareness alone is usually a recipe for failure. Governance done for the sake of governance is a hollow endeavor that rarely finds enough leadership or grassroots support to gain or sustain momentum. If it does, it often becomes a law unto itself with little regard for the analytic needs of the organization. Such governance may be worse than no governance at all.

The purpose of data and analytics is to serve the strategic goals of the organization. So, the first step in developing a new data governance program is to identify the organizational priorities. This requires an understanding, for example, of the top five targets the executive team wants everyone in the organization to help deliver over the next year. These could include lofty goals, such as increase patient engagement or improve performance-management analytics. They might also include more specific goals, such as decrease adverse drug events by 5 percent or increase patient use of telehealth services by 9 percent.

A simple list of the organizational goals, however, is likely insufficient. Effort should be made to understand the rationale behind the goals, the discussions that led to them, who was involved, and the nuance of their positions.

Health Cares Unique Challenges

While it is always good to take best practice examples from other industries, it is also prudent to understand the unique aspects to the industry you are in. Healthcare has several dynamics that make data governance more complex and challenging than other industries. Lets take a high-level look at some of these:

  • Breadth of systems needs
  • Unlike other industries unique systems for many individual areas of practice and associated services abound. Having massive numbers of systems often causes confusion and similar tools being deployed for the same need. This is often magnified by the geographic dispersion of offices i.e. identical areas of practice in two different sites may have completely different systems for billing, registration, Electronic Medical Record , etc
  • Systems costs while all industries have the need for comprehensive solutions to run their businesses, it is rare that other industries incur software costs like what the healthcare industry does. While almost all industries need ERP vendors, healthcare also has the EMR that frequently cost more than $100M to implement.
  • Data breadth and depth the type of data that can be collected in healthcare is extremely wide and very deep. This includes extensive volumes of data with a variety of measurements, lots of views of data, and complex data types . There are also various standards and vocabularies that tie/connect to clinical data that must be considered.
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    Why Is Data Governance In Healthcare Important

    These days, digital information is gold. This is true in many industries, including healthcare. Organizations strategically use health information in all kinds of ways, including to improve operational efficiency, reduce costs, or enhance the safety and quality of patient care.

    So to understand why data governance in healthcare is important, you must think of health data as a strategic asset. Like any other organizational asset , this information requires ongoing monitoring. Data governance provides a formal structure for data management so organizations can extract clinical and business value.

    Simply stated, data governance in healthcare is important, because it is vital for caregivers and leadership to have access to the right information at the right time and in the right format so that proper clinical and business decisions can be made.

    How Do Data Governance And Information Governance Impact A Healthcare Organization

    What is IT governance?

    Data and information underpin nearly every activity within a healthcare organization, but some areas of operations and clinical care depend on robust data integrity and governance more than others.

    Patient safety is a top priority for every provider, and poor data governance can quickly create critical problems. From the moment a patient sets foot in the office or consult room, his or her data must be accurately identified and must follow the patient throughout every interaction.

    However, even the basics of matching a patient to the correct record is not as easy as it seems without a quality data governance approach. More than half of health information managers encounter patient matching issues on a frequent basis, most of which must be manually addressed.

    Duplicate records and inappropriately merged records from separate individuals are among the top threats to patient safety.

    Severe patient-care issues can occur and resources are wasted when systems are inundated with duplicate records, AHIMA said. Patient safety is a major concern for many organizations, yet it is necessary to increase awareness of the safety, legal, financial, and compliance concerns created by duplicate and overlaid medical records.

    EHR data integrity shortfalls routinely feature on industry patient safety hazard lists, and may add to the frustrations of clinicians already wrestling with electronic interfaces that compound their data entry problems and may lead them into sub-optimal practices.

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    How To Get Started In Information Governance In Healthcare

    HIM professionals can help organizations get started with information governance by identifying areas of data integrity risk, such as:

    • Duplicate records. Hospitals must provide comprehensive training for patient access staff as well as initiate master patient index cleanup projects.
    • Inconsistent EHR templates. Hospitals must evaluate all existing templates and standardize them for patient care, core measure and quality reporting, and to ensure patient safety.
    • Lack of internal coding guidelines. Hospitals must develop internal guidelines to ensure system-wide coding consistency.
    • No information asset inventory. Hospitals must create a centralized source that includes the asset name, type of information , database owner, operating unit or department, retention period , and more.

    These are just a few of the information governance projects that HIMT professionals can tackle. Getting the ball rolling with information governance is often the hardest part, but the good news is that HIMT professionals are ready to take on the challenge.

    Five Manageable Steps To Healthcare Data Governance

    As a subject with many facetsfrom data security to data quality to data stewardshipits beyond the scope of this article to define the details of data governance. Data governance is more than a little overwhelming, so much so that many organizations struggle to get started or they do start, but the scope is too broad, making it difficult to gain traction.

    According to data management expert Will Bryant, Surveys report that as many as two-thirds of all initial data governance efforts die on the vine. Bryant explains that these efforts mushroom into such complexity during the planning and design phases that they are abandoned before they have the chance to deliver any value.

    Healthcare organizations can navigate the complexity of data governance set-up by following five practical, manageable steps:

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    Data Governance: A New Twist On An Old Concept

    Data governance is a fairly new term, but its not a new concept in healthcare. In fact, information governance has been at the core of what health information management professionals do every daythat is, serve as the guardians of protected health information, helping to ensure its accuracy and protect its privacy and security.

    So what has changed within the industry to shine a spotlight on data governance?

    Most noticeably, the volume of big data has grown exponentially thanks to electronic health records and interoperability. Organizations can manipulate and analyze data within seconds, allowing them to gain insights that were never possible before.

    Access to information has also grownparticularly as organizations strive to engage patients through health information technology. As interoperability continues to grow, patients, providers, and other stakeholders must ensure that information is accurate and reliable.

    Finally, new privacy and security risks have emerged in an electronic environment, prompting organizations to anticipate and proactively mitigate risk. All of this requires an organization-wide, top-down approach that aligns information with organizational strategy, ensuring that accurate information is available.

    Identify And Recruit The Early Adopters

    Establishing Data Governance

    The next step is to survey the list of intersecting priorities identified in step 2, note the accountable leaders for each and identify which of these leaders are likely early adopters of data governance. Energetic leaders who truly understand the importance of data governance should be top of mind. Once each group establishes its governance objectives, these leaders often naturally become the champions of the organizations new data governance program.

    Creating a list of desired characteristics for a data governance leader launches the search for early adopters. Geoffrey

    Moores book Crossing the Chasm offers valuable insights into important characteristics of effective early adopters:

    • Connections: Early adopters need to be connected to the right resources.
    • Enthusiasm: Because early adopters motivate others to get on board, they must understand what needs to be done and why and be excited to get to work.
    • A deep understanding of data governance: Early adopters should understand data governance on more than a casual level and be aware of the challenges and benefits. Such awareness fuels the drive necessary to break through the inertia early in the programs development.

    As the list of candidates grows, some questions that probe each persons understanding of the benefits of data governance will help narrow down the list to those most likely to carry the data governance program across the finish line. Consider questions such as the following:

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    Healthcares Urgent Demand For Balanced Data Governance

    According to the HealthIT.gov, by 2015 more than 80 percent of U.S. hospitals had figured out how to collect their clinical data. But, just as health system data has increased, so has mistrust in their data. According to a 2018 survey, less than half of healthcare CIOs have strong trust in their data. They often dont know how their analytics are derived or on what data they are based, and they rarely have any easy way to alleviate their concerns.

    Balanced data governance builds trust and effectiveness. In years past, analytics teams emphasized data security. Protecting data was paramount, and it continues to be important today. But what good is data if only a few people can use it? To paraphrase Henry David Thoreau, that governance is best which governs least. Effective organizations secure their data from misuse. Beyond that, their job is to liberate data and facilitate its best use by everyone.

    Data Governance In Health Information Management

    by Angela Guess

    Paula Mauro of the Journal of AHIMA recently wrote, Health information management principles that support timely, accurate and complete data collection and release are part of the key to meeting recent National Quality Strategy goals aimed at improving healthcare service delivery, patient health outcomes, and population health. The HIM professionals role in meeting these goals is defined by their ability to combine emerging technologies with innovative processes, says Bonnie Cassidy, MPA, RHIA, FHIMSS, FAHIMA, vice president of HIM Innovation at Nuance. Her October 3 session at the AHIMA Convention and Exhibit will explore why HIM professionals are ideally suited to be leaders in information governance and help ensure integrity across all types of data and stakeholders. In the below Q and A, Cassidy discusses what information governance is, whats brought it to the forefront now, and the emerging ways that HIM professionals will be involved.

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    What Is Data Governance Is It The Same As Information Governance

    Data governance and information governance are two common terms that are often used interchangeably, but differ in significant ways.

    Data governance is the practice of managing data assets throughout their lifecycle to ensure that they meet organizational quality and integrity standards. Data governance is geared towards making sure that users can trust their data, which is especially important when making patient care decisions.

    As part of a comprehensive data governance program, users are held accountable for creating high quality data and using that data in a secure, ethical, authorized manner.

    In the healthcare industry, health information management professionals are often responsible for developing and overseeing data governance principles that improve the consistency, reliability, and usability of data assets while optimizing EHR interfaces to eliminate unnecessary or duplicate steps for end-users and eradicate problematic workarounds.

    The goal of these activities is to improve staff efficiency, foster an environment of accountability, and create a standardized, interoperable pool of big data that can be used for organizational improvements and higher quality clinical decision-making.

    Information governance is slightly different, according to AHIMA, but it is closely linked to data governance.

    What is EHR Optimization and How Does It Start?

    What Is Health Information Governance

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    Health information governance includes the overall management of the availability, quality, integrity, and security of the data being used. Sound health information governance includes a governing body or council, a defined set of policies and procedures, and a plan and resources to execute those procedures. Many times, the terms information governance and data governance are used interchangeably. For the purpose of clarifying this initiative, they are defined as follows.

    Health information governance provides the business context in which data is controlled. It is a business or compliance/legal driven approach to managing and controlling how all enterprise content is used, retained, and destroyed.

    The success of health care reform depends on high quality, trusted data, which can be readily and appropriately accessed and shared.

    In Colorado, there are multiple organizations and systems, both public and private, housing health information. Many of these organizations share information, but there are many that cannot effectively and efficiently share their health-related data outside their own organization. This often results in multiple inconsistent “sources of truth” for health data, which result in lack of trust in the data, overlap of requests for data, incomplete information available, lack of integration of clinical and claims data, and overall difficulty in sharing health information necessary for improving the quality and cost of care.

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    What Does Data Governance Mean To Healthcare Organizations And Why Is It So Crucial To Master Before Engaging In Big Data Analytics

    Before the advent of the electronic health record, the vast majority of this data was trapped in file folders and bankers boxes. This static data resource, often obscured by illegible handwriting, missing papers, and mistakenly misplaced records, was of little use to data scientists seeking innovative ways to leverage history to predict the future of patient care.

    As the industry reluctantly adopted EHRs, stakeholders started to explore the new frontier of big data analytics. Driven by the prospect of payment reforms that promote quality over quantity, providers began to clamber for automated tools to help them understand clinical risk, communicate with their patients more effectively, and perform the population health management tasks that will help them succeed in a new financial landscape.

    But when it comes to big data, old habits die hard. The haphazard data governance practices that made paper records such a nightmare were often magnified, not ameliorated, by the switch to an electronic environment.

    Convoluted and unintuitive EHR interfaces made data entry an immensely frustrating task for overwhelmed clinicians. Health information managers struggled to walk the line between the benefits of structured templates and their inability to capture the full story of the patient. Technical experts often took a slapdash approach to EHR optimization, fixing problems however and whenever they could without necessarily thinking about the long-term implications.

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