ResolveData - Actualizing Data to Drive Transformational Healthcare
ResolveData - Actualizing Data to Drive Transformational Healthcare
More healthcare organizations are breaking down data silos as part of their transformation journey – ResolveDatas
More healthcare organizations are breaking down data silos as part of their transformation journey

More healthcare organizations are breaking down data silos as part of their transformation journey

Data Analytics

More healthcare organizations are breaking down data silos as part of their transformation journey

Many healthcare businesses face the problem of disparate data sources, which are stored in different warehouses and run on old systems that can’t handle data as quickly as newer alternatives. Because of concerns about data availability, access, analysis, and adoption, they have been hesitant to consolidate records and migrate from legacy systems. However, with advances in technology such as artificial intelligence (AI), machine learning, and computing power capable of analyzing massive data sets at a low cost, now is the moment to free healthcare data from silos.


Data unification has a lot of power


Some businesses are already using modern technology to bring diverse data together to enhance healthcare results.

Take, for example, health insurance. Health payers have been tasked with combating alarming amounts of fraud, waste, and abuse, which are estimated to cost the sector as much as 10% of annual healthcare spending, or $380 billion per year. The endeavor to detect and reduce fraud is primarily carried out by Special Investigations Units (SIUs), and fraud is typically detected after payment, requiring a lengthy process to evaluate, investigate, and reclaim recoverable funds.

However, recent technological breakthroughs have ushered in a new era of healthcare cost management. Beyond fraud and post-pay, a more holistic approach is taking shape that now includes a pre-pay focus. Despite historical hurdles such as segregated data, organizational dynamics, stringent compliance rules, and obsolete technology, many payer departments are collaborating more transparently to address these issues, resulting in significant cost savings and better healthcare stewardship.

For example, employing a holistic view of payer, provider, and patient data, healthcare insurance companies are successfully aggregating data from several data warehouses, transaction systems, and claims sets for AI analysis across numerous departments. The consolidated data is assisting in the detection of increasingly sophisticated and widespread schemes, the detection of billing and payment irregularities, and the identification of patient linkages across all providers. It also allows companies to provide a snapshot of a patient’s medical history, allowing them to search for treatment irregularities and gaps that could cause problems with ongoing treatment or services.

A comprehensive approach to data allows health plans to see tendencies that waste money, either intentionally or unintentionally, and to integrate disparate claim types to provide prescriptive insights. Some health insurers use centralized data in a variety of ways. For example, Blue Cross of North Carolina recently took steps to integrate its member experience data.

To aggregate and analyze member input, the healthcare insurer used several channels, vendors, and listening platforms, resulting in fragmented customer intelligence. Many companies are working to aggregate listening data into a single source for holistic perspectives, lowering expenses and increasing member happiness.

Transparency leads to better health results


Creating a uniform data reference for all healthcare stakeholders will be critical in moving the sector ahead toward greater openness and improved patient care and treatment. Imagine a future in which different health insurance companies could share patient records from disparate data sources like doctor’s offices, pharmacies, dentists, and surgeons, as well as rehabilitation treatments — these insights into a holistic healthcare record would make it easier to recommend and identify treatment plans for patients, thereby improving quality of care.

There are chances to shift the healthcare paradigm from fee-for-service to value-based treatment as more healthcare software businesses enable secure access to comprehensive data. Better benchmarking and population health management are also possible with access to a large common healthcare dataset.

Integrating data for the greater good


People and businesses in other areas are already benefiting from data integration. Consumers can get a holistic perspective of their personal finances by using apps that combine account data from banks, credit cards, and brokerages, for example. For years, IT companies such as Cloudera, Salesforce, and Databricks have offered specialized data collecting, unification, and analysis solutions that interact with organizations’ preferred tools. These tailored solutions combine data from several platforms to provide businesses with a comprehensive end-to-end picture of their systems, allowing them to better their operations and offerings.

Healthcare plans and insurance companies that take on the task of combining all provider, patient, and claims data in a single platform are rewarded with easily available healthcare data analytics that gives transparency and insights to all stakeholders.

Integrating Large-Scale Healthcare Data


When it comes to getting data reports, many end users think of lengthy processes, bespoke requests, and significant return times. However, two ways can assist health systems in streamlining the healthcare data integration process and delivering the most relevant data to end users more quickly:

First, choose infrastructure that meets the needs of the majority of people. Rather of designing a data solution for one person or one group of individuals, health systems should invest in data infrastructure and capabilities that meet the demands of the majority of their decision makers. Data infrastructure that meets the broader demands of decision makers allows more individuals to use data as soon as the infrastructure is in place, avoiding the delays that come with customizing alternatives. Data teams can quickly expand a commercial solution still having the ability to customize it as needed.

Leaders should select a variety of the organization’s use cases and then research analytics solutions they can quickly apply to those use cases to determine the optimal data infrastructure for the health system.

How to go about the Data Integration


Data and analytic leaders must first ask the proper questions in order to determine which data will be most beneficial to decision makers. Data and analytic leaders, for example, should review which data sets end users can now access, then inquire about which additional data sets they are asking from data support teams (e.g., what custom reports is IT generating). Inquiring about extra data requests will disclose which data sets team members require but are unable to use in their decision-making.

Data teams can establish a data report “menu” with multiple data report options that anyone in the organization can request after they have a better awareness of the organization’s data needs. A data menu can be compared to a fast food menu for executives. Customers can request multiple sorts of cheeseburgers from a restaurant’s menu, for example. Leaders can also develop a menu based on the most common requests/needs of decision makers and use standard language that end users and technical team members understand.

Creating a streamlined data report menu lowers custom report requests and delays in reporting caused by technical jargon misunderstandings. End users and technical team members can order data reports from a menu, which makes the process go more smoothly.

Rapid Improvement is a result of effective healthcare data integration. Health systems must make data and supporting tools available to their team members as data and data-based technologies (e.g., data displays) become more commonly employed in healthcare. If end users can’t quickly access data at the point of decision making, it won’t be able to fulfil its full potential—informing decisions that improve and restore patient health.

Leaders can invest in data infrastructure that benefits the majority of end users’ demands and provide a simple menu that team members can use to request data reports by asking questions and researching team members’ wants. Leaders who offer data to decision makers when it is needed maximize data in workflows, processes, and day-to-day care.

Data is being shaped by experts at ResolveData to give better results. We collect, harmonize, and analyze data. Our mission is to assist you promote and drive activities that result in better health outcomes for your patients, such as more efficient care delivery, better clinical results, and lower health-care expenditures. For more details, talk to our experts today.

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ResolveData - Actualizing Data to Drive Transformational Healthcare
  • ResolveData - Actualizing Data to Drive Transformational Healthcare
  • ResolveData - Actualizing Data to Drive Transformational Healthcare
  • ResolveData - Actualizing Data to Drive Transformational Healthcare
  • ResolveData - Actualizing Data to Drive Transformational Healthcare

© 2021 ResolveData. All Rights Reserved

© 2021 ResolveData. All Rights Reserved

  • ResolveData - Actualizing Data to Drive Transformational Healthcare
  • ResolveData - Actualizing Data to Drive Transformational Healthcare
  • ResolveData - Actualizing Data to Drive Transformational Healthcare