Healthcare and the need for a robust Data Governance Model

“If you can’t measure it, you can’t improve it”

Who knew the words of Peter Drucker would resonate so well in current times.

With the world entrapped in the clutches of the COVID-19 pandemic, we are seeing the use of data and analytics in healthcare move to a new dimension. Data is being used on a minute by minute basis for measuring the infection rate and impact of the pandemic by governments and health institutes. The results are being used to continuously adjust policies and every day public measures. Each day the data collected is solving new mysteries about this virus but also opening up new questions to be answered.

Data has found extensive use in healthcare even in pre-COVID times. Over the years data has been playing a key role in the digital transformation of healthcare.

• Data collected in the form of EMR (Electronic Medical Records) helps improve the speed of diagnosis and the efficacy of treatment for the patients. Availability of EMR also makes online heath consultation feasible, which is the need of the hour during this pandemic time.

• Predictive Analytics for potential health risks and diseases is another area where extensive research work is ongoing. The use of data and predictive analytics assists healthcare professionals to initiate treatments in the early stages of various life threatening diseases. Universities such as Mellon and Pittsburgh in the U.S. are using huge volumes of data that is analyzed to predict outcomes of treatments for cancer patients.

• Bio-surveillance systems with real time alerts allow clinicians to detect potential infections in advance. This helps further in reducing the rate of hospital re-admissions.

One such example is the prediction of hospital-acquired infections. In Denmark over 3000 people die due to infections post-surgery or due to hospitalization each year. Hospitals in southern Denmark are now relying on the monitoring systems that not only provide a clear picture of the rate of infection but also help take preventive measures in advance to reduce the rate of infections.

Some Key Challenges in the Healthcare Domain are:

• Data Quality is Crucial – Data quality is critical as it directly controls the model and the outcome that could have life and death implications. One of the most important steps is to ensure the data entered by clinicians is accurate and they completely understand the implication that it could have on the results. The use of data and technology is to be seen as an assistance to the healthcare professionals and not as a threat. Their expertise and experience is still pivotal in healthcare, with AI and data being tools they could use to become more efficient.

• Security and Safety of Data is paramount – Security of data is important as the data shared in healthcare is highly sensitive and access needs to be policy driven.

• Speed of Data Retrieval is of utmost importance – Data retrieval needs to be fast and reliable, or the impact can be critical to patient health.

The need for Data Governance Models

With the heavy reliance on data for healthcare improvements also comes great responsibility to ensure the quality of data being used is not compromised. Hence, there is a need for a strong data governance model in place to ensure that.

Data Governance Model and Healthcare

An observation by Forbes in 2017, makes impossible for someone to ignore the importance of Data Governance in Healthcare…

“The vast amount of data generated and collected by a multitude of stakeholders in healthcare comes in so many different forms —insurance claims, physician notes, medical records, medical images, pharmaceutical R&D, conversations about health in social media, and information from wearables and other monitoring devices. Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet”

Forbes, 2015

Data collection has been given an immense push during this pandemic situation. Data on proximity, compliance and tracking of test results and such have added new paradigms in data management, mining and deriving insights.

However, despite the proliferation of data, most organizations find it hard to optimize data assets to deliver sophisticated and practical insights.

With such vast magnitude of data, it is imperative to adopt a robust process and framework to manage this Big Data. Hence, the importance of a Data Governance Model becomes all the more important.

A data governance model as per KPMG in its report ‘Data Governance: Driving value in HealthCare’ has four components as depicted:

In its efforts to bring the gains to Industry, IBM vouches for a Unified Data Governance Model. This Model can help organize the disparate data in flexible ways so as to enable a higher level of efficiency in its retrieval regardless of its context. Similarly, in the Healthcare domain, there is the Micro-Level Data Governance, there is the Macro Level Data Governance (Where data governance adheres to the rules and policies of a nation-state in delivering patient and health practitioner care) and then there is the Universal Data Governance Model. All need to quintessentially work in unison.

In bringing efficiency and effectiveness in treating a patient, a continuously improving Data Governance Model is key.

“Strong data governance ensures that the right information, of the right quality, is available to the right person, for the right purpose, at the right time.”

Evan Rawstron, KPMG Global Healthcare D&A Lead

Sources:

https://www.worldometers.info/coronavirus/ https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/emerging-topics-health-care-109815.pdf https://home.kpmg/xx/en/home/insights/2018/06/data-governance-driving-value-in-health.html