The primary goal of data governance is to ensure that data assets meet an enterprise’s standards in terms of its integrity and quality. In an organization, data may be sourced or generated from across its departments or related entities such as vendors and customers.
It is of utmost importance that data thus generated be in a trustable, reliable format for its users. This is especially so for the pharma and health care industry, which relies heavily on such data to undertake patient care decisions.
Big Data and Analytics
For the health care and pharma industry, only precise, trustable data can lead to insightful decision making. Precise data analytics tracks not just aspects such as resource wastage or performance of medical practitioners and other health care personnel. Data Analytics’ robust, dynamic metrics capabilities if properly applied is capable of tracking health data or chronic conditions or disease risks among the entire population of people.
Data Governance – definition by AHIMA (American Health Information Management Association)
AHIMA defines Data 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”.
Data Vs Information
Multiple units of data are combined to form Information. This information is used for analysis and eventually, insightful decision making.
Importance of Data Quality
- Data quality affects operational profits and clinical care and patient deliverables:
- When a patient enters any health care center, his/her safety and precision diagnosis entirely depends on:
- Accuracy of patient’s medical data entered/retrieved
- Continuous follow-up throughout the patient engagement life cycle
- Only by the presence of a standard Q&A system for data governance, can eliminate issues such as patient Vs data mismatch
- When a patient enters any health care center, his/her safety and precision diagnosis entirely depends on:
- Another significant data governance aspect affecting positive patient outcomes is the redundancy of patient records or possibly duplicated or merged patient records, which may create confusion
- Duplicated patient records create such sensitive situations that affect end-to-end parameters such as patient safety, financial aspects, legal aspects, and compliance aspects
- The incorrectness of patient data entry is one of the most significant aspects that hugely impact the security, treatment, and quality of therapy provided to patients
- Incorrect or incomplete data about patient allergies, historical medical procedures, and other drastic medical conditions, etc. immensely affect the diagnosis and treatment is provided. They may often end up as huge threats to patient’s health
- Gaps in the quality of Electronic Health Record (EHR) data very frequently remain as causes of patient health risks
- Many times, cloning of patient’s medical documents end up as frustrations for health care Managers. These may display either incorrect or outdated patient historical information throughout the diagnosis and treatment being given. This aspect poses a huge threat to patient safety
- Quality of data governance is significant for undertaking regulatory programs by the health care unit/pharma. It affects the following parameters in a big way:
- Big data analytics
- Conducting population/community health programs and their management
- Health care quality and performance benchmarking
- Reporting
Robust data governance and quality systems in pharma and health care enterprises not just paves say for seamless clinical care, but also makes it more reliable for the patients. These are the key drivers of explicable patient outcomes and experiences as well as the productivity of the enterprise.
Use of Data Analytics in Patient Journey
Today’s digital technology world is viewing novel, high-tech, and innovative ways of direct patient data collection. Patient expectations and resulting in patient experiences (PX) delivered are reaching path-breaking levels of innovations. Healthcare and pharma enterprises have already implemented and using patient-focused ways of healthcare deliveries, evaluating and deriving their outcomes as well.
- Usage of data analytics in patient journey facilitates observing patient interaction with the health care center and gathering data at every stage of their collaboration to effectively analyze it for superior patient deliverables. It starts from data gathered from the stage of diagnosing symptoms, the advancement of the disease, its therapy, and obliteration
- As and when a patient traverses through the healthcare journey, they get to select unique, novel digital, overwhelming options of treatment methods
- Sum-total of the patient data and information is stored in APLD (anonymous patient level data) sources such as EMR data sets, digital prescriptions and digital claims information
- Data analytics methodologies, within seconds shifts logically across billions of data, set rows
- These data are then processed by the data analytics tools to bring out actionable insights for medical practitioners, pertaining to patient medical condition diagnosis and treatment
Developing Consequential Health care Data Governance Strategy
- The initial step to put in place a data governance plan is to identify its need at every level of the organization, right from the highest to the basic level
- Most successful and productive health care enterprises are those that implement data governance at every step of their corporate structure
- Such enterprises provide all possible types of support for their information management personnel at every possible way to achieve impeccable data governance quality
- Putting in place a Core Data Governance Team:
- Health Care enterprises must include the participation of all types of personnel and /stakeholders as a part of their strategic data governance team
- Stakeholders include not just HIM or EHR specialists, but also every field staff including physicians, nurses, paramedics, financial personnel analysts from the big data team
- Every stakeholder in the team should clearly enlist their data governance goals, create their own benchmarks
- Every member of the team must clearly be provided with the set of responsibilities, be an active participant in all meetings and interactions, take feedback from peers seriously and work on them
- Last but not the least, an attitude of team-work and cross-departmental interaction is a must for every member to achieve the overall high-quality data governance goals
Access to precise, and consistent quality health care information is the fundamental right of every individual on the planet.
AHIMA Recommendations for Quality Enterprise Data Governance
- Operate an information and data gap analysis plan:
- Identify departmental and individual challenges in data entry, storage, analysis and subsequent reporting
- Service providers should focus on challenges that may affect patient privacy safety, as well as regulatory compliances
- This will significantly eliminate occurrences of harmful outcomes in health care operations
- Enlisting a set of guiding principles for quality data governance that is compliant with the Service provider’s mission, vision, and fundamental health care values
- Creation of policies and procedures, with intent focus on engagement rules for members, accountability, member roles, their authority, cross-departmental disputes, rights of decision making, change management, cost management, and overall health care value creation
- Start a Clinical Documentation Improvement (CDI) program for training personnel on the importance of creation of quality data right from the basic, initial stages
Ameex expert team emphasizes adherence to all the above-provided points for good data governance. For consultations and more information, write to us.
