Data always plays a significant role in the healthcare industry. From surgical records and pressure reading to immunization history and payment receipts, every action of every member in the healthcare ecosystem depends on endless data.

Previously, most of this data used to be trapped in folders, files, and bank boxes. The data resources, often obscured by mistakenly misplaced records, missing papers, illegible handwriting, were of little use to data scientists who want to introduce innovative ways for leveraging history for predicting the future of patient care.

But the industry has now started exploring the frontier of data analytics. Big data is taking the world to the next level. With several industries, it is also transforming healthcare.

insights from the data and apply the learning to complex initiatives that can impact their reimbursement rate. If healthcare organizations are able to integrate data-driven insights successfully into their operational process, the outcome can be highly beneficial.

Lower costs, healthier patients, and better visibility in performance are among some of the benefits of turning data assets into data insights. However, the road to meaningful healthcare analytics can be a rocky one, but, it is filled with challenges and problems to solve.

management is the practice to collect, keep, and use data securely, cost-effectively, and efficiently. As you already know, data held by healthcare organizations are of incredible value. In fact, it is the most valuable asset they have. Thus, data governance in the healthcare industry is non-negotiable.

Data governance is about managing data and processes to make sure it is secure, organized, and consistent that meets standards and policies. When implemented and scaled properly, data governance can transform healthcare organizations.

But with the increase in data, the mistrust in data has also increased. Balanced data governance is required for building trust.

Healthcare data governance is different than those faced by the other industries. However, the lack of maturity in areas such as data governance or taking data as an asset continues to be a problem. The healthcare industry is not yet equipped to adapt to the massive data influx. This is putting them in a difficult situation.

According to a 2018 survey, less than half of the healthcare CIOs have a robust trust in their data. They do not know how the analytics are derived or what the data is based on.

As per a study conducted by AHIMA in 2015, around 33% of the participants agreed that they have not gone ahead to promote data governance as a business imperative, whereas one-fourth of the participants said that governance was never a priority is their leadership role.

Patient safety is always the topmost priority for every healthcare provider and bad data governance can create serious issues. As soon as a patient comes to the hospital, the medical data should be precisely identified. But without a good data governance process, even matching a patient’s medical records is a difficult task. Nearly 50% health information managers face recurring issues related to patient data mismatch which has to be manually rectified in the end.

As per AHIMA, duplicate and overlaid medical records cause serious patient-care health issues. Even after having the correct record paired with the right patient, healthcare quality gets suffered if the patient record is outdated or incomplete. Outdated or missing data related to current medications, allergies, or past surgeries can affect the treatment negatively.

Healthcare data is the data used by medical professionals for making informed decisions regarding patient care. Data governance will provide healthcare organizations with a structured and standardized method of sharing data for offering the highest quality care to each patient.

Data management is the primary component of an all-inclusive data management strategy. With this healthcare organizations can identify trends and provide better healthcare.

The problem is due to the nature of the data. Changing clinical research data sets is a useful source of information but they come with many problems. An overreliance on the data sets might hurt organizations in terms of generalizability.

The ways in which studies are conducted does always match with real-world conditions that make the findings at times difficult to implement in clinical settings. So, data cannot lead to knowledgeable inferences that create big data quality problems. Without proper data governance, inconsistencies might crop up in various systems across organizations. This might lead to incorrect information and a delay in treatment.