The Value Data Operating Systems to EMR Data Migration Best Practices

Powering Up Healthcare Data with The Use of Data Operating Systems

Data operating systems (DOS) are powerful tools that companies use to make use of data in order to improve their businesses. By leveraging DOS, organizations can gain insights into customers, operations, and more that can be used to enhance the customer experience and streamline business processes. With a DOS in place, companies can have access to real-time data from both internal and healthcare IT leaders today, and they are grappling with critical challenges such as population health, value-based care, declining revenues, and rising costs. Addressing these challenges requires robust data solutions, real-time access to data, scalable analytics platforms, and seamless system interoperability.

According to TechTarget’s 2023 IT Priorities Study, 41% of healthcare organizations boosted their information technology budgets this year to bolster cybersecurity measures and advance cloud computing initiatives. This urgency stems from the 295 breaches already reported in the healthcare sector in 2023, affecting over 39 million individuals and major firms like Managed Care of North America.

50% of HCOs plan to invest in email security this year.

To counter the escalating cyber threat, 50% of surveyed healthcare organizations plan to invest in email security within the following year. Additionally, nearly half of these organizations intend to implement proactive measures like vulnerability management by year-end.

Despite the wealth of electronic medical record (EMR) data, the value of EMR data migration best practices is not commonplace, leaving some EMR data inaccessible for analysis. Notably, 80% of EMR data is unstructured, including valuable physician notes outside the analytics system. Data from disparate sources, including social determinants of health, cost, care management, and biometrics, significantly influence health outcomes but often remain isolated.

EMR and claims data are collected and transformed into reports and applications in the present analytics landscape. However, this process can become convoluted in terms of content and logic. For instance, different reports may employ varying criteria to identify patients with diabetes using ICD-10 codes. (See Table 1)

Adding complexity, the EMR operates independently from the analytics infrastructure, making presenting insights to clinicians at the point of care challenging. This monolithic system lacks customization options and limits users to a single configuration.

Furthermore, diverse healthcare roles require distinct data for various purposes:

Multiple stakeholders with varying priorities.

- IT departments need data for reporting and application demands.

- Clinicians require real-time insights within their workflows.

- Population health leaders seek data scalability.

- Financial leaders rely on data to navigate revenue/cost challenges.

- Merging health system leaders requires data migration.

- Independent software vendors need clinical data for integration.

The analytics environment encompasses multiple data sources, necessitating custom connectors for data acquisition.

However, each new data source introduces new complexities and further isolates the data.

Electronic Medical Records (EMRs) were initially designed for fee documentation but fall short when it comes to data analysis. Some analysts are turning to Data Operating Systems (DOS) to address this. DOS excels in three areas:

1. **Technology & Data Type**: It employs cutting-edge technology and AI to transform raw data into analysis-ready content.

2. **Purpose & Utility**: DOS enhances quality, reduces costs, and improves patient and provider experiences by capturing clinical and operational data.

3. **Better Workflows**: DOS generates insights, enabling actionable change within organizations.

Standardizing data accelerates value creation, streamlining reports and application development without redundant data mapping.

DOS integrates shared data marks encompassing clinical claims, patient satisfaction, cost, person, and medical terminology, breaking down data silos.

DOS analysis converts raw data into comprehensive insights through five steps: combining related and raw data, identifying trends, enriching with shared content, adding AI/machine learning, and producing deep, meaningful, and actionable data.

Here is a table that explains how these fit by transforming raw data (single patient with diabetes) into deep data that predicts risks and suggests interventions:longitudinalcomplications,

Raw Data	Combine	Trend	Enrich	Predict	Deep Data
CD-10 Code = E11.65 on 06/01/2017 LONIC Lab Code=4548-4 Value 8.2 10/12/2018	The patient has diabetes, and the lab is HBA1c. Diabetes is also on the claims and in previous records.	HBA1c increased by 30% in the past six months.	Patient should be placed in the Diabetes registry. The patient is failing CMS measure 59.	This patient is at high risk for developing compilications. Patients like this may respond to titration of insulin.	​Searchable, longititudial, meaningful, and actionable.
Ingest using the Health Catalyst Analytics Platoform	Meaning via terminology. Patient matching via token. Add related data via aggregration	Create a longititudial record.	Apply shared content to data.	Run AI models. Apply expert best practices.

Summary

DOS assimilates data from 200+ sources, leveraging analytics platforms and data models, enabling real-time data integration, and aligning report logic. This closed-loop system ensures data remains current while APIs extend its functionality. Healthcare leaders and software vendors can harness DOS to drive transformative change and boost financial, clinical, and operational outcomes. DOS can be swiftly implemented despite its extensive capabilities, delivering value within months.