Health Information Along With The Rise Of Medical

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Welcome to the Data Analytics Toolkit PowerPointpresentation on data governance. The complexityof healthcare delivery, the exploding demand foractionable information, pressure for greater publicaccountability, and the ever-changing legal andregulatory environment require quality data andinformation for decision making. Technology is theenabler of many changes, and the rapid adoption ofinformation and communications technology inhealthcare is changing how health information ismanaged. Every day industry stakeholders seesigns and symptoms signaling the need tostrengthen health information management andgovernance. The persistent breaches of personal

health information along with the rise of medicalidentity theft and healthcare fraud are obvious andtroubling examples. Data integrity and qualityproblems in the paper medical record are reincarnated and magnified in clinical informationsystems, and error correction is markedly morecomplex. The inconsistent ways in whichunstructured data are managed and used, limits itsusefulness. Effective data and informationgovernance is imperative to the meaningful use ofhealth information systems (Kloss, 2013).Information governance addresses who isresponsible for what information and who can makebusiness decisions about an information asset.Meaningful Use requirements are driving thecreation of increasingly more data in healthcare,and healthcare organizations need to implementpolicies and programs to govern the use of this datato ensure data integrity.

Information governance and data governance areoften used interchangeably; but despite thecommonalities, there are also key differences.Information governance considers a broadperspective of health information issues, while datagovernance focuses on actual data elementscollected in the medical record. InformationTechnology or IT governance focuses on IT systemsand resources specifically. Information governanceincorporates both data and IT governance (Dimick, 2013;Kloss, 2013).

Let’s look first at information governance.Information governance is led by executiveleadership. It is an accountability framework anddecision rights to encourage appropriate behavior inthe valuation, creation, storage, use, archival, anddeletion of information including processes, roles,standards, and metrics (Logan, 2010). Decisionrights are a system of determining who, when, how,and under what circumstances a decision is made(The Data Governance Institute, 2004-2008).Information governance addresses who isresponsible for what information and who can makebusiness decisions about an information asset(Thomas Gordon, 2014).

Data governance is led at the business unit leveland is a component of information governance.Data governance has an operational focus includingan emphasis on policies, processes, and practicesthat address the accuracy, validity, completeness,timeliness, and integrity of data (Warner, 2013;Dimick, 2013).

Lastly, IT governance includes policies andprocesses to ensure the effective evaluation,selection, prioritization, and funding of competing ITinvestments in accordance with the organization’sstrategy. The focus is on making technologydecisions and determining IT strategy according toinformation needs (Dimick, 2013).

Healthcare IT generates more data than ever beforeand attention to how this data is used is imperative.The purpose of data governance is to establishorganizational structures, roles, policies, andprocedures to manage information as a strategiccorporate asset; to ensure consistent and propermanagement of data across the organization toimprove data quality and data integrity; and to instilla higher level of confidence in information used indecision-making (Walton, 2013).

Data governance has three objectives to ensuregreater accountability for quality as well as moreconsistent definitions for data. Data quality issuesare often identified after reports are run. A datagovernance program includes methods forproactively identifying data quality issues beforethey become visible or used for decision-making.As data quality issues are discovered, they need tobe resolved at the root of the problem which is oftendue to poorly defined rules or workflows in thebusiness unit in which the data was collected. Datagovernance ensures well-documented processesand responsibility for data quality in the properbusiness unit. Many data quality issues are caused

by inconsistent data definitions. Data governanceestablishes a structure for various roles andresponsibilities to ensure consistency of datamanagement standards (Walton, 2013).

There are various roles in a data governanceprogram. The executive decision makers areadministrators from across the organization. Theymay be part of the governance committee thatidentifies strategic goals, provides direction on dataownership, and makes resources available forgovernance activities (HIMSS, 2013). The dataowner is usually at the director level and has fullaccountability for one or more types of data. Thebusiness data steward is the subject matter expertin a particular area and has the most important role.This person is most knowledgeable regarding abusiness unit’s data and solutions to any dataquality issues. The technical data steward uses

tools to identify data quality issues and implementsprogram code fixes that have been approved by thedata owner. The gatekeeper monitors the status ofall data quality issues and tracks them until they areresolved (Walton, 2013). Data advocates ensuredata can be easily accessed, help the organizationwith reporting data and using analytical processes,and help promote a data driven culture (HIMSS,2013).

Key items that should be included in a datagovernance program are standards, processes,organizational responsibilities, and technologies.Standards for defining data definitions, taxonomies,master reference data and enterprise data modelsare needed. Processes are necessary formanaging data definitions, data quality, data changemanagement, and data access. Organizationalresponsibilities for data governance oversight,planning and prioritization, training, and roles andresponsibilities must be identified. Technology isneeded for managing data dictionaries, master datamanagement tools, data access and discoverytools, data manipulation tools, and data integration

tools (HIMSS, 2013).

Establishing consistent policies, standard operatingprocedures, and a clear monitoring system toensure accuracy and availability of data should bepart of the data governance framework as well asdefining the organization’s information assets (Reno& Kersten, 2013).

When developing an information governanceprogram, use a data-dependent business need toraise awareness of the value of the organization’sinformation assets to create a vision for agovernance program. Convene a steeringcommittee and assign key roles; be sure to engageexecutive leadership in the steering committee.Other working committees should also bedeveloped. Consider all functions of the informationlife cycle when defining the scope of the governanceprogram; information design and capture, contentand record management, information analysis anduse, data integrity/quality, access, security, andconfidentiality. Conduct an assessment of the

current environment for all identified areas. Forexample, are policies and procedures in place andin use? Develop a timeframe considering initiativessuch as the Meaningful Use program. Consider aphased approach and develop realistic timeframes.Finally, take an incremental approach, identifyingpriority areas to focus on first.

There are some important considerations whenestablishing a governance program. Be sure toobtain executive sponsorship so that you have theauthority to move forward as needed. Empowerdepartment managers to make data decisions sothey can feel an ownership in the governancestructure. Promote ‘quick wins’ as much as possibleso momentum can be created. Identify gaps inrealizing strategic objectives and identify potentialrisks early so that the potential challenges are notedas soon as possible. Be sure to focus on improvedproductivity, quality, patient satisfaction, andstrategy alignment.

There are also cautions to be aware of. Be sure to communicate clearly,effectively, and often to stakeholders. Monitor and control data and resolveidentified issues as soon as possible. Do not miss the alignment or ‘fit’between business strategy and organization culture. Attention to the strategyand culture is key to success. Also, integrate data governance with other dataprojects so that the connections are apparent.

The benefits of effectively governing all this data that healthcare organizationsare gathering in their quest for Meaningful Use of health IT include theability to describe how, when and by whom data was received, created,accessed, modified, and/or formatted,ability to determine whether data is fit for its intended use,ability to ensure security and privacy compliance across integrateddepartments, and theability to provide a logical structure for communicating complexactivities and making decisions

The challenges in effectively governing data may berelated to resource availability and management,ineffective project teams, ensuring a focus on datagovernance, having clear authority, obtainingexecutive buy-in, and ensuring clear alignment tothe business strategy.

When initiating and developing an effective datagovernance program, keep in mind that a successfulprogram will require additional resources both intime and money; and senior leadership must besupportive initially as well as continually in order tosolidify the importance of properly managinghealthcare data as a business asset.

Information governance considers a broad perspective of health information issues, while data governance focuses on actual data elements collected in the medical record. Information Technology or IT governance focuses on IT systems and resources specifically. Information governance incorporates both data and IT governance (Dimick, 2013; Kloss .

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