How Interoperability in Healthcare is Helping Patients and Improving Quality of Care

How Interoperability in Healthcare is Helping Patients and Improving Quality of Care

Healthcare costs have been on the rise ever since the late 90’s. In fact, they have almost doubled over the last decade, especially in the USA. The Health IT sector, however, has tried to address these concerns related to cost in innovative ways. Reducing wait times, eliminating room for error and improving the quality of healthcare in general has drastically improved the affordability of care. Interoperability in healthcare has lead this change.

Here are some ways healthcare data and application interoperability can help patients by improving patient outcomes, cutting costs, and aiding value-based care.

Reduce Wait Times and Improve Quality of Care

With interoperability, patient information across several systems is available to doctors at any point in time. This makes collecting, analyzing, and processing data a breeze as opposed to sending out an email to a blood bank or a lab for related patient info, thereby reducing patient care and wait times.

A good example of a Microsoft Azure solution is the Novari ATC. It provides an efficient flow of information across systems in the OR to facilitate better surgical wait times and e-bookings of cases in real time. By reducing OR and other wait times through effective data and application interoperability, doctors and administrators are able to efficiently care for patients. This leads to a higher success rate with treatment. In short, interoperability in healthcare has a considerable positive impact on patient experience.

Eliminate Room for Duplicate Records

A study conducted at the Children’s Medical Center in Dallas showed that duplicate records increased patient cost by over $1000 on average. Creating a standard access point for patient data eliminates this massive expenditure on duplicate tests, prescriptions, and reports.

A good example, is the OLIS (Ontario Laboratories Information System) system in Ontario – a province-wide central data repository that stores lab tests and other health documents that can be digitally transferred.
Dapasoft has integrated many acute care provider EHR system including Cerner, Meditech, and Epic to name a few so that lab data is easily accessible in a central repository.

Improved Patient Engagement

The increased use of mobile health apps in the past decade have given patients the chance to access their health records remotely and even communicate with their provider. This is where data standardization kicks in, to make sure that data moving from one system to another is converted and streamlined smoothly. Patient Engagement stops patients from visiting/revisiting and has show to aid the quality of care in general.

Dapasoft helped William Osler Health System (WOHS) setup their MyChart patient portal in just about 18 months by leveraging the hospital’s existing BizTalk Server. The portal saw over 18,000 patients sign-up and helped improve patient engagement and outcomes.

Interoperability in Healthcare Allows for Movement of Massive Amounts of Data

While the security of data on cloud-based systems and mobile devices is vulnerable to breaches, it allows for a large volume of data storage. A recent Cisco report showed that health data will grow nearly four folds in the next five years and that in turn becomes a task to manage manually. With interoperability, this data can be moved through various systems and platforms, which indirectly helps keep patient data segregated while allowing for massive storage.

A good example is NEODIN (Northern and Eastern Ontario Diagnostic Imaging Network), a regional diagnostic image repository that allows for the storage of X-rays, MRI, CT Scans, ultrasounds and other records from over 62 hospitals in one secure central repository.

5 Ways Interoperability in Healthcare Can Help Providers Cut Cost And Improve Care

5 Ways Interoperability in Healthcare Can Help Providers Cut Cost And Improve Care

The Healthcare industry, much like many others, has been the latest to go the IT route. IT in healthcare has the potential to help providers in improving operations, implement feasible solutions, and improve the quality of care. IT has also brought to light the importance of health data analytics. Important data can now be used between disparate systems to improve treatment for patients, expedite the time-consuming process and eliminate any errors that restrict the quality of care.

Here is how interoperability in healthcare is helping providers improve quality of care and other relevant parameters.

Access to Health Data

Health Data is futile when not accessible by the right people at the right time. It is important for providers to be able to access a patient’s data when required. This becomes a task when data is stored in disparate systems. Healthcare interoperability streamlines the data and harmonizes the exchange over several systems.

Improved Analytics

Accessible health data from different wings allows for accurate health data analysis and helps in predicting possible outcomes of patient treatment. The information can also be used to look at the success rate of medication, patient history, and other aspects of care.

Cutting Costs

With interoperability in healthcare, providers have access to comprehensive medical data, eliminating any room for duplicate or loss of tests, reports and scans. This, in turn, helps providers cut administration cost and prevents patients from spending on duplicates.

Eases Research

With data readily available, the task of searching, validating, verifying and compiling data becomes a lot easier. This will help providers study treatment, healthcare delivery and conduct other research work as and when needed.

Monitor And Improve Services

Interoperability gives healthcare providers a Tsunami of data at their fingertips. This data can help them take important steps to monitor and improve specific services. That, in turn, helps in improving the quality of healthcare and overall patient experience.

The Amalgamation Of Healthcare and IT in a Nutshell

The Amalgamation Of Healthcare and IT in a Nutshell

Not long ago, the world of IT took on the health industry to change it for the better. With the help of the internet and rapid advancement in technology, IT has managed to help overcome the many challenges of the health industry to make efficient, effective, affordable, error-free care of high quality an everyday phenomenon.

Although the Health IT sector has been a theory since 1995, its importance has come to light only in the last couple of years. A sudden explosion in the last decade happened mainly due to the many challenges that tedious, outdated health care processes have posed along the advancement and increased accessibility in modern-day tech.

Healthcare solution by AzureHealth IT was brought about with a motive to move administration and patient care to a digital platform, which in turn could cut costs and improve patient care. Digitizing several aspects and processes has shown to cut administration and patient costs by a staggering 50% as compared to the old-school way of things. This comes through tedious paperwork, storing documents, duplicate test reports, malpractice claims and other redundant processes.
Apart from cutting costs Health IT has also proven to make value-based care a reality by using patient history and other vital information to chart a graph of the patient and to some extent, provide precautionary treatment.

Healthcare solution by Azure

With this and several other solutions it provides in bettering healthcare, Health IT has garnered popularity on an international scale for adaption. Several countries are now adapting and incorporating Health IT into their systems, which involves some technical work such as data integration and interoperability to make data transfer seamless and streamline many of the cost-intensive ways of storing and using healthcare data.

Medical data storage, transfer and integration is what gave birth to the IT aspect of health care and its rapid development is estimated to grow to a global market of over $400 billion in the next 10 years.

Data Lakes – The Next Big Thing in Healthcare IT ?

Data Lakes – The Next Big Thing in Healthcare IT ?

In the age when every keystroke on your keyboard or swipe on your phone is tracked the era of Big Data is thriving. The advent of Microsoft Azure in 2008 allowed the Healthcare Industry to finally have access  information that, up until that point, had only been accessible via large companies such as IBM. The ability for the Healthcare Industry to pull information based on mass amounts of accurate data was nothing short of revolutionary.

The advent of this mammoth data machine altered the face of both the for-profit and non-profit sector.  It changed the way nearly all organizations worked and created entirely new industries. With the addition and popularity of mobile applications in the late 2000’s the business of tracking data all but exploded. Soon preventative health was being tackled by companies such as Fitbit which created a personal activity tracker which measures and tracks heart rate, sleep activity and number of steps walked.

Data Lakes - The Next Big Thing

The flood of data coming in, literally, from all corners of the world was organized into countless institutional Data Warehouses. Early industry predictors indicated that this mass amount of data would lead to healthcare researchers quickly uncovering information that could lead to cures or treatments. While this newfound data assisted greatly, flaws in the Data Warehouse concept were soon discovered.

The modern concept of the Data Warehouse began in the late 1980’s. IBM’s Systems Journal article published in 1988 coined the term “business data warehouse”. Bill Inmon (the ‘father’ of data warehousing) began to discuss Data Warehouses as far back as the 1970’s and in the early 1990’s published the industry bible Building the Data Warehouse. Inmon’s model for data warehousing concentrates on a centralized data repository.

Healthcare providers and researchers began to realize that this model meant accessing the data proved much more difficult and often it was not helpful to their research.  The main issue they faced was that the Data Warehouses were designed and controlled by a diverse range of operators. These individual operators could range from hospitals to research centres. These Data Warehouses employed the concept of ‘schema on write’, meaning that the data is organized as it is added to the warehouse. In fact, data is not even loaded until its eventual use is determined. For healthcare providers and researchers this method meant that they had to rely on countless institutions and their respective warehouse designs.  The information culled from disparate Data Warehouses produced at times inconsistent and conflicting data. Also, the ‘schema on write’ method prevented data from being entered in a timely manner; all information would first have to be surveyed and analyzed through individual systems. Healthcare leaders realized what they needed was access to unstructured data that they could analyze on their own timeline.

The concept of Data Lakes was born.

Data Lakes - DapasoftA Data Lake is a storage system that is able to hold mass amounts of data, but unlike the Data Warehouse with its structured, hierarchical format, the Data Lake holds raw data intentionally eschewing up-front formatting to provide users unfiltered access to the most up to date information. Data Lakes use the concept of ‘schema on read’; data is not analyzed until the end-user accesses it.

Therefore, with Data Lakes at their disposal the Healthcare Industry are not constrained by institutional schemas. While it is logical that hospitals worldwide have created their own Data Warehouses based on their own understanding of what was required by the front-end user, naturally each institutional Data Warehouse would be managed by different teams of people whose intake process for the Data Warehouse can inherently cause wide gulfs in how information is analyzed. In contrast, the Data Lake allows users to pull raw healthcare data unburdened by (if well meaning) ineffective filters.

Data Lakes provide numerous advantages over Data Warehouses for the Healthcare Industry beyond data capture.

Healthcare spending in Canada now runs into the billions of dollars annually. A portion of this cost is infrastructure spending to operate Canadian healthcare institutions including their IT operations and data storage. Adopting the use of Data Lakes greatly minimizes the costs associated with data capture and storage. Not only do operators save costs on the physical assets required for storage, but they can avoid the cost of hiring specialized staff for schematic design and data input.

Data Lakes also allows practitioners to provide patients with Precision Medicine.  Precision Medicine is an emerging medical concept that proposes tailoring healthcare to individual patients. Using Data Lakes and previously mentioned health applications such as the Fitbit personal health tracker, the ability for capturing unfiltered health information from individuals and its timely analysis can now have immediate impact for patients. By its very definition, Data Lakes provide the most open, agile format for end users.

The Healthcare Industry can now take advantage of Data Lakes supported by Microsoft Azure.

Azure Data Lakes will enable the Healthcare Industry to create repositories where their data can be held without constraint. Data of any size or format can be held at a much lower cost, and these savings can be used toward providing improved patient care. Health practitioners and researchers can also access data in real-time increasing the speed in which to apply this knowledge to produce real-world results. The Azure Data Lakes also enable users to invest in new technology without concern that this investment will not sync with their current Data Warehouse.

Big Data provided the Healthcare Industry volumes of structured information that influenced practitioners and researchers alike.  Azure Data Lakes is the bold next step and the future of Healthcare Data.

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