Running Head: DATA GOVERNANCE FRAMEWORK 1

Introduction

The healthcare industry is adopting digitization for an immense amount of clinical,

financial, and other involved operational data. To effectively perform health analytics, every

healthcare setting needs to integrate data from complex applications such as electronic health

records, laboratory systems, ERP systems, pharmacy systems, or other affiliated healthcare

providers such as external laboratories or benchmarking consortia (AHIMA, 2015). In this

complex environment, data governance practices are required to ensure that all data used for any

analytic output is accessible, well understood, trusted, and protected (CIC, 2015). This calls for a

framework that will govern this process of information exchange. The intended audience for this

data governance framework includes healthcare executives, managers, and data managers in the

pharmacy department.

CHARTER FOR INFORMATION/DATA GOVERNANCE PURPOSE

The main purpose of this charter is to classify, organize, and communicate complex activities

necessary when making decisions about the drug dispensing of a particular patient. The

document captures all the information architecture and data governance practices necessary for

the pharmacy department, in all healthcare settings. Data governance committee (DGC) main

purpose is to facilitate the data use for efficient decision making by pursuing the following

primary goals;

 Information governance: To bring all clinical, financial, and technical partners together to

design and optimize data in alignment with the organization's goals. This also includes

data security of data accessed within the scope of this organization and applicable data

governance regulations and legal standards.

DATA GOVERNANCE FRAMEWORK 2

 Quality: Effective clinical decision making is achieved by timely, complete, consistent,

and timely data. DGC ensures that data quality is maintained using standardization such

as data quality metrics monitoring. This enhances positive outcomes in clinical and

financial operations

 Usability: this entails designing user-friendly systems that enhance data interaction

between organizations to enhance organizational performance. DGC ensures that they use

tools that include training and data dictionaries.

 Availability: DGC aims to implement an appropriate analytical infrastructure that will

ensure data is rapidly available when needed irrespective of the clinical operational

needs. This will ensure that decision making is based on evidence-based practice

DEFINING STAKEHOLDERS

A stakeholder is a person or organization that has an interest (direct or indirect) in data

from this particular organization. They include the Chief executive officer, head of departments,

legal team, risk management team, finance department, quality improvement, physician, nurse

managers, registered nurses, nurse practitioners, and human resource manager.

Functions and responsibilities include

 Ensuring senior leaders commit to the implementation process of active data governance

infrastructure and effective improvement strategies by providing the necessary

operational, clinical, and technical expertise.

 Providing insights into the whole process of data governance functions by adopting the

IG principles of healthcare including accountability, integrity, transparency, protection,

retention, compliance, and disposition. The overall of this project is to get good data to

the end-user so that they can make the best clinical decision-making process.

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 Approving and reviewing the information governance policies and identifying the

burning platform once the project is launched. Data governance is tied to the real clinical

world to realize the presence of data gaps or disruption so that it can be analyzed based

on the organization metrics.

 Approving all information and data governance linked road maps and strategies.

Although the DGC does not function as gatekeepers, their role is to ensure end-users

have transparent data for their jobs.

 Prioritize the information governance scope, initiatives, and priorities

 Coordination all data governance management responsibilities in the organization

 Evaluate the progress and outcomes

SUBCOMMITTEES:

All information technology team including health information management, data privacy

and security team, information content team, research committee, and business analytics

intelligence.

Functions and responsibilities include:

 Constructing architecture of the advanced business intelligence (BI)

 Quality data extraction from the many source systems into a single EDW (single source

of trusted data)

 Data extraction automation and reporting

 Modeling of advanced data definitions and common vocabulary

 Issuing the end-users with advanced analytical tools that will help them make good

decision making and improve outcomes

DATA GOVERNANCE FRAMEWORK 4

 Perform the ongoing improvements necessary in data governance to ensure that the end-

user manages the data needs effectively.

 Educating the end-user on data governance to empower them with advanced analytical

skills needed to optimize their performance

 Create a data-driven mentality in this organization.

REPORTING:

Reporting will be made to the board committee by the DGC chair. The report will

comprise of committee reports, minutes, and any other significant documents that capture

matters to do with information governance or data governance.

AUTHORITY:

The chain of command must be followed as authorized by the organization when

reporting matters concerning data governance. All authority lies within all members of the

committee. Subcommittees will be formed based on the expertise needed to complete a task:

MEETING FREQUENCY:

DGC committee meetings will be held quarterly whereas the subcommittee will be done

monthly or on-demand. The monthly meeting is aimed at ensuring that stakeholders keep

updated on all matters regarding data governance. Any additional meeting will be based on

organization data governance need.

PLANNING AND IMPLEMENTATION

DATA STEWARD APPOINTMENT

 Data stewards will include the CEO, directors, department supervisors, and IT data

analysts. The roles and responsibilities of a steward include;

DATA GOVERNANCE FRAMEWORK 5

 Ensuring accountable management of all data assets, lineage, and access in the

organization by supporting efficient data analysis and rationalization of the data

information.

 Focusing on data strategy, support and execution of data programs, data apps, and

production

 Ensuring data remains accurate and of quality by performing profiling, querying,

analysis, and mapping of data qualities.

 Defining standards and best practices needed to sustain quality data and when performing

queries and modeling.

 Working in collaboration with other organizations to ensure that data collected is of

quality, is complete, and timely data collection and documentation.

DATA GOVERNANCE PLANNING AND COMMUNICATION

The main aspects of data governance planning are determined by the WHO-WHAT-

WHEN-WHERE-WHY-HOW essentials of data governance. They include:

WHO – Includes all the names of the involved stakeholders including their organizational

roles and responsibilities, as well as their engagement with the data stewardship. They include all

stakeholders interested in how the organization creates, collects, process, manipulate, store, or

retire data.

WHAT – Data governance in this framework defines the act of decision making and

authority for all data related information. In this case, the framework explains data governance

including detailed information explaining the organization bodies, data governance rules and

regulations, how-to-decide, accountabilities, controls, evaluation, and enforcement methods for

information governance initiatives.

DATA GOVERNANCE FRAMEWORK 6

WHEN – Provides the key schedules that determine the need for data governance. In this

case, data governance will be required when organizations get large data that is no longer able to

manage data related functions using the previous traditional management. It will also be applied

when the organization data systems become so complex that traditional management cannot

adequately manage all the data related functions. More so, if the DGC feels the need to improve

data systems so that they can support cross-functional programs that will provide an enterprise

view of data choices. Lastly, if formal data governance regulations and contractual requirements

call for additional changes.

WHERE – Gives the location of data including their scorecards and reference documents.

The location for this organization is the Information technology (IT) team as they are responsible

for Application developments, identify the data related concerns that call for data governance

attention, and are charged with completion of data governance projects on time and within the

budget frame.

WHY – the organization data is rapidly increasing in volume. Information governance is

necessary to ensure that data stored is of high quality and accurate for effective decision-making

processes. Information management for enterprise data calls for trusted data. The data is used to

map the business intelligence, therefore, the intelligence is influenced by the quality of the data

gathered and the effectiveness in managing it. The framework will organize how DGC reason

and communicate about data-related issues or associated ambiguous concepts.

HOW – Explains how the process of data governance benefits the organization, including

how the success will be evaluated. The first step is to determine the data governance goals and

development of the value system. The scope of the project is established using SMART goals. A

road map of the efforts is developed, and it will be used to acquire all the support needed from

DATA GOVERNANCE FRAMEWORK 7

the DGC and involved stakeholders. The program is then designed, monitored, and assessed to

determine its outcome. If successful, staff training is conducted, if not, the team goes back to the

drawing board to determine why it failed and areas of improvement that are needed.

Effective communication is one that is:

 Concise: Brief to the point and straight forward to ensure efficient action

 Complete: Contains all the necessary information

 Clear: Well illustrated to ensure easy comprehension

 Considerate: Open to feedback, questions or clarifications

Timing: The message timing will be determined by the urgency and the message that

requires to be communicated. In this organization, the most convenient method of

communication is through E-mail. However, for detailed and complicated messages, an

interactive approach will be used such as zoom meetings. All questions asked will be followed

up including any other clarification required.

DATA GOVERNANCE TRAINING PLANNING

 Training will be used to promote all effective information related to data governance to

the organization staff.

 Training will be done to make sure that each staff in the organization understands their

roles in data governance including handling of data, accessing data, and how the data is

retained.

 All of the staff are required (mandatory) to attend the training session. In-job training and

refresher training will be undertaken regularly to ensure that all staff remains up to date

with upcoming innovations.

DATA GOVERNANCE POLICIES

DATA GOVERNANCE FRAMEWORK 8

 All policies are placed on the applicable practice regulations, standards, and legal

frameworks.

 DGC will also formulate the organization’s inventory policies.

 DCG will be responsible for the policy frameworks outlining the data governance

implementation process and the enforcement policies. It will also outline all the

disciplinary measures when the policies are not adhered to.

Administrative policies

 Risk management preparation and data recovery mechanism.

 Governs all the process of data retention, storage, and disposition

Health information management policy

 Patient health data documentation including photography, patient portals, and HPI

management.

 Management of patient identification

 Approval of medical forms

 Use, disclosure and amendment of PHI

Information system policy

 Manage all cloud-based information

 Organize all data sets, standards, and clinical vocabularies

 Management of information system access, passcodes, and downtime

 Management of all mobile devices

Privacy and security policy

 Maintain data set confidentiality by overlooking all privacy practices

 Dispose of all documents, materials and all other data governance information

DATA GOVERNANCE FRAMEWORK 9

 Perform access audits of PHI

 Fax all confidential data information

 Ensure PHI security

Risk management policy

 Collect, store and protect all data used in this organization

 Perform E-discovery for data used in this organization.

DATA GOVERNANCE PROCESS

The data governance process will be done in 12 steps organized into 5 phases as shown in

Fig 1.

Fig 1: Data governance process (Source: Data Governance Institute, 2018).

Phase 1: Charter establishment. It includes WHY essentials of data governance frameworks as

indicated in processes 1 of Data governance framework in Fig 1 including;

 Identification of stakeholders

DATA GOVERNANCE FRAMEWORK 10

 Development of mission statement in alignment with the organization policies and

organization

Phase II: Planning phase. It includes WHO, WHAT and HOW essentials of data governance

frameworks as indicated in processes 2 to 3 of Data governance framework in Fig 1 including;

 Development of project’s scope statement

 Developing a plan based on decision rights and accountability

 Presentation of the plan to the steering committee

 Approval of the presentation

Phase III Execution. It includes WHEN and WHERE essentials of data governance frameworks

as indicated in processes 3 to 9 of Data governance framework in Fig 1 including;

 Developing accountability and stewardship performance

 Managing data changes based on defined data issue and conflict resolution strategies

 Integrating data governance into technology

 Data governance training

 Organization implementation of the data governance

Phase IV: Monitoring. It includes processes 11 Fig 1 including;

 Publishing of the status report communicating to the involved stakeholders

 Updating project based on the status report feedback

Phase V: End phase. It includes process 12 in Fig 1 including;

 Verification of the final project

 Post-project evaluation

Data governance Schedule

DATA GOVERNANCE FRAMEWORK 11

Phases WK1 WK

2

WK3 WK4 WK5 WK6 WK7 WK8 WK9 WK10 WK11 WK12

Phase

1

Phase

II

Phase

III

Phase

IV

Phase

V

DATA VOCABULARIES

Data elements Description

Pharmacists ID unique identifier for pharmacist

Primary practitioner name Name of the primary practitioner

Primary practitioner ID unique identifier for primary practitioner

Telephone number telephone at which pharmacy can be contacted

Address Addresses used to contact the person of interest

Date date of drug dispensing

Time time of drug dispensing

Remarks Any extra information not captured during drug dispensing

Patient ID Unique patients identifier

Patient Name Patient Name

Primary practitioner Patient’s physician's name

Health Status Patient's health status e.g. diabetic mellitus

Drug prescription Drugs prescribed to patient

DATA GOVERNANCE FRAMEWORK 12

Dispensed /Remarks Drug has been dispensed or not

DATA GOVERNANCE FRAMEWORK 13

References

AHIMA. (2015). Information Governance Toolkit. American Health Information Management

Association. Retrieved from https://bok.ahima.org/doc?oid=301510

CCI. (2015). Data Governance Handbook. Retrieved from Center for Care Innovations.

Retrieved from https://www.careinnovations.org/wpcontent/uploads/2017/11/CCI-Data-

Governance-Handbook.pdf

Data Governance Institute. (2018). Depart of behavioral health and disability services, City of

Philadelphia. Retrieved from https://dbhids.org/wp-content/uploads/2019/02/DBHIDS-

DG-Framework-Strategic-Plan-v2.03.pdf

Data elements Pharmacists ID Primary practitioner name

Field description unique identifier for pharmacist Name of the primary practitioner

Valid values Numeric Text

Data format NNNN NNNN

Data Size 8 5

Key Primary Primary

Data Source Person Person

Example 12528963 Dr. James. D

Data elements Patient ID Patient Name

Field description Unique patients identifier Patient Name

Valid values Numeric Text

Data format NNNNN NNNN

Data Size 10 10

Key Primary Primary

Data Source hospital person

Example P659685 Mrs. M.G

Data Dictionary for Facility and Staff identification in Pharmacy

Data Dictionary for patient identification in Pharmacy

Primary practitioner ID Telphone number Address 1

unique identifier for primary practitioner telephone at which pharmacy can be contacted pharmacy address 1

Numeric Numeric Numeric

NNNN NNNN NNNN

8 10 110

Primary Primary Primary

Hospital Hospital Hospital

96325843 978-969-4343 Corporate Headquarters. 343 Business Place. Suite 314. Seattle, WA 98112. 

Telephone number Address 1 Primary practitioner

Number to contact patient Address to contact patient Physician's number

Numeric Numeric Text

NNNN NNNN NNNN

10 12 12

Primary Primary Primary

hospital hospital hospital

978-444-5555 Fake Street. Seattle, WA 98112. Example 2. 349 23rd Avenue. Burlington, WA 98233 Dr. Tricia Smith

Data Dictionary for Facility and Staff identification in Pharmacy

Data Dictionary for patient identification in Pharmacy

Date Time Remarks

date of drug issue time of drug issue Any exta information not captured

Numeric Numeric Text

NNNN NNNN NNNN

6 4 110

Primary Primary Primary

Hospital Hospital Person

20.05.2020 0230HRS Patient allergic to penicillin

Health Status Drug prescription Dispensed /Remarks

Patient's health status Drugs prescriped to patient Drug has been dispenced or not

Text Text Text

NNNN NNNN NNNN

110 110 110

Primary Primary Primary

Hospital Hospital Hospital

Diabetes Metformin 40 mg Dispenced 100 tabs

Data Dictionary for Facility and Staff identification in Pharmacy

Data Dictionary for patient identification in Pharmacy