information governance life cycle

HIM 350 Module Four Journal Assignment Guidelines and Rubric Overview: This journal assignment requires you to reflect on the information governance life cycle. Information governance is the structure and policies that govern how data is collected, organized, secured, and utilized. Every activity is backed up by data and information. A lack of governance around this data and information can lead to problems. Healthcare facilities need reliable, accurate, timely, and accessible data in order to make better informed decisions. AHIMA defines information governance as “an organization-wide framework for managing information throughout its lifecycle, and for supporting the organization’s strategy, operations, regulatory, legal, risk and environmental requirements.” This journal assignment focuses on discussion around information governance plans and the information governance life cycle. Journal assignments in this course are private between student and instructor. Prompt: Within the journal assignment, answer the following questions: •    What are the steps in the information governance life cycle? What is an information governance plan? How does an information governance plan address data quality? Why is it important for healthcare facilities to create an information governance plan? Rubric Guidelines for Submission: Your journal assignment should be 2–4 paragraphs in length, with any sources cited in APA format. Submit assignment as a Word document with double spacing, 12-point Times New Roman font, and one-inch margins. Critical Elements Steps Proficient (100%) Explains the steps in the information governance life cycle Information Governance Plan Defines what an information governance plan is Data Quality Discusses how an information governance plan addresses data quality Importance Articulates the importance of healthcare facilities creating an information governance plan Needs Improvement (55%) Explains the steps in the information governance life cycle, but with gaps in detail, clarity, or accuracy Defines what an information governance plan is, but with gaps in detail, clarity, or accuracy Discusses how an information governance plan addresses data quality, but with gaps in detail, clarity, or accuracy Articulates the importance of healthcare facilities creating an information governance plan, but with gaps in detail, clarity, or accuracy Not Evident (0%) Does not explain the steps in the information governance life cycle Value 23 Does not define what an information governance plan is 23 Does not discuss how an information plan addresses data quality 23 Does not articulate the importance of health care facilities creating an information governance plan 23 Critical Elements Articulation of Response Proficient (100%) Includes no errors or minor errors related to organization, grammar and style, and APA citations Needs Improvement (55%) Includes some errors related to organization, grammar and style, and APA citations, but errors do not impede understanding Not Evident (0%) Includes major errors related to organization, grammar and style, and APA citations that impede understanding of the submission Total Value 8 100% Working Smart a professional practice forum Navigating Privacy & Security / Illuminating Informatics / Standards Strategies / Road to Governance Information Governance for Analytics Support: Remember the Life Cycle Component By Shannon H. Houser, PhD, MPH, RHIA, FAHIMA; Donna J. Slovensky, PhD, RHIA, FAHIMA; and Luona Wang T THE VOLUME AND types of healthcare information created and captured grow constantly and exponentially, and the business, regulatory, and service drivers for information use continue to evolve as well. Both volume and importance of healthcare information compel robust and sophisticated information management practices and systems to ensure accountability, accuracy, and validity of information as a valuable organizational resource. Much of the value of this resource emerges from an organization’s ability to optimize information through sophisticated analytics approaches to inform clinical and business decision support, and as a tool for organizational learning. At the most basic level, the goal is to secure exactly the data needed—the best and most accurate version—in the most costeffective manner possible, and to transform that data into the information needed to ground clinical and business decisions. Ideally and simply, data are captured once for use as many times as needed to generate numerous types of information. This requires storing the data in a format and location that allows accessibility on demand. Realistically there is nothing simple about securing, managing, and protecting the data and information resources of a healthcare organization, and transforming an organization’s data into useful and reliable information increases in complexity with advances in informatics tools and methodologies. In fact, Big Data is big business, encompassing multiple professions and industries, and consuming vast amounts of money. Current best practices in information management favor a governance approach that encompasses an organization’s policies, business and clinical processes, and operational practices as well as the technology and infrastructure required to capture, use, and manage healthcare information over time.1 An organization’s information governance plans define a framework for 38 / Journal of AHIMA June 17 oversight and accountability, and address the myriad problems that challenge information management, such as security and confidentiality, regulatory compliance, legal discovery, storage optimization, data migration initiatives, and others. However, many governance plans address one component insufficiently—the information life cycle. This omission is problematic because availability of current and relevant data is essential to ensuring analytical models produce reliable insights to guide decisions and strategies. Information Life Cycle Models Conceptually, the information life cycle is linear, beginning with data creation and ending when the data are deleted—a “birth” to “death” definition. However, the traditional healthcare approach has been to capture and keep all data, migrating it to newer systems and rarely purging any archived data. This practice by default results in a circular life cycle model (see Figure 1 on page 39), with no exit ramp or endpoint to the model as data are never deleted or destroyed by plan. The model is deceptively simple visually, but living it actually is quite complex. The massive volumes of data in healthcare and the business and service drivers for data use compel organizations to employ sophisticated information management systems, most of which have evolved over many years. These systems rarely have been designed in their entirety in response to the organizations’ planned information needs and intended use for analysis. Instead, they have emerged incrementally, with each new iteration layered onto existing systems, often migrating old data into newer systems. Sometimes older systems are retained and connections established to new systems, or older systems continue to operate independently of new ones. New information is con- Figure 1: Circular Information Life Cycle Model stantly created and added to existing information, resulting in more information than is absolutely needed or will be used. Organizations manage an overwhelming volume of data and experience unplanned data redundancy and data inconsistencies through multiple capture points and manipulation. While retention periods are legally defined for many categories of healthcare data, cost-effective digital storage options have allowed organizations to avoid making comprehensive data deletion decisions. It has been easier and affordable to migrate data to new formats rather than to define a data destruction plan and implement it, which also has associated costs. The model in Figure 2 above is slightly more complex than the first model, and includes a destroy decision, but still omits nuances that are critical to robust information resource management, such as data validation, security backups, and value assessment prior to making the archive or destroy decision. Because current technological capability gives so many options for cost-acceptable data capture and storage, many organizations haven’t been adequately motivated to make structured business decisions from a life cycle perspective that are responsive to information expansion issues. They haven’t distinguished among data that are “needed,” data that are “wanted,” and data that are “just there,” and have no sound basis for resource investment decisions made to ensure optimal information accessibility to meet business and clinical needs.2 Maintaining large volumes of data not verified for currency or relevance challenges the optimal deployment of analytics tools. Thus, skilled informatics and analytics expertise are needed to select the “best” data from among redundant sources to inform analytical models, and more time and financial resources are required to clean data prior to analysis. Information Life Cycle Governance Considering the information life cycle from its definitional viewpoint as linear, incorporating components of the governance model, and involving the right stakeholders in planning can yield a robust enterprise data strategy to support clinical, Figure 2: Expanded Information Life Cycle Model business, and analytics needs. The groups below are among the important stakeholders to consider when defining the life cycle ending point for various data maintained for clinical, business, and analytical purposes: –– Business units (legal, human resources, finance) –– Research and analytics units –– Information security, privacy, and compliance –– Contract management –– Records management –– Information technology –– Patients/consumers –– Business partners In addition to noting their primary environment as internal or external to the organization, relationships among the various stakeholders are important to consider as well. Mapping the key groups and relationships among the groups, such as the example in Figure 3 on page 40, can be useful in setting priorities and making decisions about competing data requirements. Implementing an information governance model that incorporates and enforces the life cycle component can help organizations avoid two undesirable inverse data relationships:3 –– Relevance and efficiency—the more information acquired and stored the less value it has if utility and access are not ensured. –– Time value of data—healthcare data have an important and complex time relationship. For many applications, the older the information the less value it has for decision support. Some analysts suggest that information growth has reached a tipping point—information growth exceeds organizational budgets and processes for managing and governing that information.4 Since it is unlikely that healthcare organizations will stop generating data, managing this imbalance will require more aggressive deletion and selective archiving practices as key components of the enterprise data strategy and governance plans. Journal of AHIMA June 17 / 39 Working Smart a professional practice forum Navigating Privacy & Security / Illuminating Informatics / Standards Strategies / Road to Governance Figure 3: Stakeholder Model 3. 4. 5. 6. Achieving optimal information resources to support an organization’s clinical, business, and analytics needs requires commitment and collaboration throughout the organization, beginning with leadership and cascading down to the lowest information user level. At its core, the primary goal of such a program should be “defensible disposal” of data based on assessment of legal and regulatory guidelines, as well as the information’s value to the organization in consideration of business objectives, clinical needs, and goals for organizational learning through analytics. 5 While “defensible disposal” cues to legal incentives or mandates for retaining data, some business analysts propose that only one percent of data generated is actually subject to legal hold, and only 25 percent has ongoing business value.6 While these numbers likely differ significantly when considering healthcare data, it’s evident that great volumes of “digital debris” are currently stored and maintained at significant organizational cost in all industries. In addition to the financial costs, the loss of business efficiency and compromised data quality result in other costs that are less easily quantifiable. An attentive focus on defining information life cycle termination points will enhance the effectiveness of an organization’s information governance programs. ¢ Notes 1. Empel, Sofia. “Way Forward: AHIMA Develops Information Governance Principles to Lead Healthcare Toward Better Data Management.” Journal of AHIMA 85, no. 10 (October 2014): 30-32. http://bok.ahima.org/ doc?oid=107468#.WNA5_vKHrig. 2. Willig, James H. “The many lives of data.” Handbook of 40 / Journal of AHIMA June 17 Healthcare Management. Cheltenham, UK: Edward Elgar Publishing, 2015. Kahn Consulting, Inc. “Information Lifecycle Governance.” Information Governance Brief. www.kahnconsultinginc.com/images/pdfs/Brief%20-%20Information%20 Lifecycle%20Governance.pdf. Paknad, Deidre and Rani Hublou. “Information Lifecycle Governance Leader Reference Guide: A Model for Improving Information and eDiscovery Economics with Information Lifecycle Governance.” CGOC. 2012. www.slideshare. net/DanielDAngelo4/information-lifecycle-governanceleader-reference-guide-58674785. Ibid. EDRM. “Disposing of Digital Debris.” April 22, 2014. www. edrm.net/papers/disposing-of-digital-debris/. Shannon H. Houser (shouser@uab.edu) is a health services administration/health informatics associate professor at the University of Alabama at Birmingham. Donna J. Slovensky (donnaslo@uab.edu) is a professor and senior associate dean for academic and faculty affairs at the University of Alabama at Birmingham, School of Health Professions. Luona Wang (luonaw@uab.edu) is a MSHA/MSHI graduate student at the University of Alabama at Birmingham’s health informatics program. USE CODE: Pssst… XW0DX82 To Report the life-saving antidote for some forms of Chemotherapy Understand the Importance of Coding New Technology for the Future of Patient Care ICD-10-PCS 10 Section n X: Brought to you by: Maximize ze Reimbursement rse e Providing Providin while g Edge Care Ca Cutting Graphics and Examples provided by: Be on o the loo lookout forr June June’s s AHIMA MA Resource Email for more re information and a Free Course! m e Promo code: AHIMARocks!17 A www.FYItesting.com/SectionX Copyright of Journal of AHIMA is the property of American Health Information Management Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use. PRACTICE BRIEF practice guidelines for managing health information Best Practices for Data Analytics Reporting Lifecycles: Quality in Report Building and Data Validation W WHILE THE IMPORTANCE of data quality in providing high-quality clinical care in today’s healthcare setting is typically well understood, the quality of data for report building and validation activities is often not well articulated—and potential data quality issues that impact the accuracy of reports are a frequent, unwanted outcome. Quality data for reporting and validation is critical to ensure that business decisions based on data have positive outcomes. As a result, data quality must be fully understood and continually managed to avoid possible false conclusions or, even worse, negative outcomes. This Practice Brief outlines best practices regarding data quality characteristics. Application of these characteristics can be applied to healthcare data to ensure success when building reports, validating data, planning methodologies, and analyzing data for both clinical and operational business needs. Data Quality Defined To understand how to improve the quality of data reporting, one first must understand what is meant by the term data quality. Data quality simply means that the data that is being reported is meaningful and serves its intended purpose. The Centers for Disease Control and Prevention (CDC) has defined the six core data quality dimensions as:1 –– Completeness. The data is comprehensive and complete. All data values are recorded. –– Uniqueness. Data is unique and one-of-a-kind. Duplicates are avoided. –– Timeliness. Data represents reality at the point in time in which it is collected. –– Validity. Data measures what it is intended to measure. –– Accuracy. Data is reflective of real-world values. –– Consistency. Data values are consistent across data sets. Data can be matched. There is no conflicting information. These six dimensions can be managed through data quality management. Data quality management refers to “the business processes that ensure the integrity of an organization’s data during collection, application (including aggregation), warehousing, and analysis.”2 Both data quality and data quality management are essential to the success of report building and data validation. 40 / Journal of AHIMA October 18 Data Collection and Report Building Building a report starts with the data collection. This is especially important and pertinent for current health information management (HIM) practices with increased information technology and rapidly growing mountains of information. The first step is understanding the purpose of the data collection, different types and sources of data, and key factors that relate to building a report. Purpose of Data Collection Healthcare organizations collect healthcare data for different purposes, including: –– The ability to compare hospitals’ performance with a peer group, especially with an organization of excellence, is beneficial in today’s competitive environment. Benchmarking has become a common tool used even at the departmental level. –– C linical decision support provides expert knowledge to healthcare providers to assist them in making the best decisions regarding patient treatment and care. –– T he Medicare.gov website Physician Compare maintains information on hospitals, doctors, nursing homes, home health agencies, dialysis facilities, and drug and health plans for the Medicare beneficiary. All of this information is available to the public. One can compare information about the quality of care and services these providers and plans offer and obtain helpful tips on what to look for when comparing and choosing a provider or plan. –– A ny healthcare organization or vendor collects and uses information to understand a population and make operational decisions with the purpose of improvement. It can be for quality, payment, productivity, accuracy, financial, resource management, or trending. Software is designed around data elements necessary to capture the information necessary for use. More health information and informatics management professionals are needed in the development of health information technology because HIM professionals have knowledge of the information they are trying to capture. Excluding HIM in decisions causes other problems instead of providing a solution, which ends up costing organizations more money. Practice Brief Data Types Healthcare data includes different data types and data from different sources. Major types of healthcare data are usually clinical data, administrative data, financial data, operational data, and population h …
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