Tag: Data

Why Has Big Data Analytics Become A Part And Parcel of Healthcare?

Why Has Big Data Analytics Become A Part And Parcel of Healthcare?

Every minute of the day, we are amazed to see how a remote patient monitoring system is gleaning thousands of pieces of health data using variable telehealth devices and digital systems like Fitbit, Bluetooth, smart phones and sensors and collecting information about things like medication…

Applications of Data Science in Healthcare

Applications of Data Science in Healthcare

Technological advancements in Data Science and Big Data have led to a revolution even in the field of healthcare. Since the amount of data being generated each moment in the domain of healthcare is enormous, big data applications, as well as data science technologies, are…

VNA and PACS: The Answers to Effective Management of Exponentially Growing Healthcare Data

VNA and PACS: The Answers to Effective Management of Exponentially Growing Healthcare Data

Healthcare systems are generating more patient data than ever before. With the progression of diseases, the “data footprint” of each patient increases over time, thereby increasing the overall amount of data, which the appropriate bodies (mostly health care providers) must manage.

Large amounts of data are inherently difficult to manage, but an abundance of data also means that better analytic results can be derived, which is necessary to drive lower cost and better patient outcomes. Consequently, there is a huge demand for data management platforms in healthcare and allied industries for efficiently storing, retrieving, consolidating, and displaying data.

A Vendor Neutral Archive (VNA) is an integral component of modern health data management. A VNA is a storage solution software that can store images, documents, and other clinically relevant files in a standard format with a standard interface.

Data stored in a VNA can be freely accessed by other systems, regardless of those systems’ manufacturers. This interoperability is a hallmark of any VNA system. The term “Neutral” in the acronym VNA has huge implications, as it makes the data stored in VNA, platform-independent. VNAs make it easier to share data across the healthcare system, facilitating communication between departments. They enable imaging clinicians to use software that integrates images with the EHR, in order to help make better-informed diagnoses.

A VNA can also help make data more secure. VNAs that use cloud-based storage can offer better recovery options than a local-only solution. Even if the local files are corrupted or destroyed, the data remains intact in a secure location through a cloud server.

Another hidden advantage of VNAs is the lowering of administrative costs. Fewer systems and fewer points of access mean less overhead for the IT department. And there is no need to migrate data when systems are updated or replaced, a procedure that can be resource-intensive. VNAs potentially offer lower storage costs, as compared to separate PACS systems, throughout the healthcare system as well. VNAs can use information lifecycle management applications to automatically shift older data to less expensive long-term storage, keeping only the most used data on higher-cost quick-access media.

Implementing a VNA is a major shift in a healthcare system’s operating procedures. This shift can uncover a multitude of opportunities to increase efficiency, streamline workflows, and lower costs.

PACS

Modern diagnostic practices generate an enormous amount of pictures and pictorial data. PACS stands for Picture Archive and Communication System. The main purpose of PACS is to simplify the management of images related to patient monitoring throughout the treatment and recovery. Modern radiology practices involve digital imaging. Therefore, for the purpose of interoperability, a standard is required, which is identified by all the stakeholders and is accepted as a norm.

The case in point is DICOM, which stands for Digital Imaging and Communications in Medicine. PACS that adhere to DICOM standards are better suited to accommodate digital image data generated through medical devices procured from different vendors. In other words, DICOM-compliant PACS have better interoperability and a wider coverage for storing and processing different types of digital images generated through varied medical procedures.

The conventional advantages of PACS include duplication removal, quick access of patients’ images and reports, remote sharing of patient’s data and reports within an organization or to other organizations, and the establishment of chronology in patients’ radiology results, in order to facilitate comparison with previous studies on same or other patients.

BEST ENTERPRISE IMAGING STRATEGY: WHAT SUITS YOUR NEEDS

With a multitude of vendors offering enterprise image management systems, it becomes difficult to make the best choice. Each organization is different in terms of organization hierarchy, as well as the type of network used for communication and financial constraints. Consequently, the requirements for enterprise imaging solutions for each one of these will be different, and no one vendor alone can satisfy all of these demands.

GE Healthcare and Philips offer some of the most exciting PACS solutions. These two vendors have a unique distinction of having a global clientele and providing enterprise archive-centric strategies. An enterprise archive refers to long-term storage for managing and collecting data from multiple imaging departments.

If organization’s needs are more VNA-centric, then vendors with exclusive VNA expertise should be considered. An example of a VNA-centric expert would be Agfa. Agfa provides VNA solutions at the enterprise level for handling both DICOM and non-DICOM data.

Irrespective of the size of one’s facility or a number of patients one has contact with, you need to make image storage a necessity, because physicians require a seamless access to them. As a thumb rule, it is imperative to say that any large organization with dedicated departments for various diagnostic imaging (or at least a dedicated radiology department) should have a PACS system in place. If financial constraints are not in place, then a hybrid system incorporating both VNA and PACS should be used for cloud-based storage. Hybrid systems with cloud-based storage are considered to be one of the most efficient modalities in current enterprise imaging management.

Source by Jagrati Mehndiratta

Data Science in Healthcare: Applications That Are Changing the Health of the Masses

Data Science in Healthcare: Applications That Are Changing the Health of the Masses

Data is everywhere and it would be foolish not to use it for the betterment of various sectors and the general public. This is the reason why the medicine and the healthcare field are using big data as an effective tool to gather data and…

How Data Science Is Shaping the Future of Healthcare

How Data Science Is Shaping the Future of Healthcare

Data and Healthcare The reason why health care is one of the prime clients of Data Science is that it has been generating massive amounts of data for ages, and now is the time to really push this data to the limits which haven’t been…

Data Science for a Better Future of Medical and Healthcare Industry

Data Science for a Better Future of Medical and Healthcare Industry

Today, technology is everything, every industry is now depending on technological advancements to increase their revenue. However, the most basic reason why industries rely on technology is to keep market competition high and offer the best customer services. One such revolutionary stream which has taken almost every other industry by storm is data science for which one can find many quality certifications.

It is all about gathering data, pruning data and then understanding the data to find meaningful insights at the end of the data pipeline. All these insights are then used to create value to the company by increasing customer satisfaction, developing new products and making existing products and services more efficiently. One such field which is now extensively using big data is medicine and healthcare.

BIG DATA IN HEALTHCARE

By 2020, healthcare data will be exceeding 2,314 exabytes. What use is of all the enormous data pile if not applied to understand the healthcare scenario better. In the past, all the medical records were stored as hard copies, but with big data emergence the records are collected, stored and interpreted via data science tools. Having structured medical data helps in better patient care and healthcare decision making.

There are three types of data that is collected by healthcare units across the globe. They are:

  1. Electronic patient records.
  2. Clinical records made by doctors, nurses in the form of prescriptions, medical reports, laboratory notes, and medical insurance companies.
  3. And machine-generated data from social media, website traffic, news data, journals and from machines showing vital signs.

DATA SCIENCE IN TRACKING PATIENT VITALS

It helps in tracking the vitals of all the patients who are plugged into different devices which keeps the track of all the vital signs like blood pressure, heart rate, respiratory rate etc. Using big data helps doctors in knowing any kind of vital changes in the patient’s body quickly without a need to monitor them personally all the time.

DATA SCIENCE IN DATA SOLUTIONS

Medical and health care industry handles an enormous amount of data on every day basis, which needs systematic collection, sorting, and systematic storing. It helps in handling the data so that doctors and medical practitioners can have easy access when in need.

DATA SCIENCE IN BUSINESS DEVELOPMENT

Almost every other hospital is part of corporate firms and groups, which is why there is a continuous need to earn revenue and make the hospitals a profitable ecosystem. It helps in creating business plans and new products which boost the business success rates.

DATA SCIENCE IN FRAUD DETECTION

Every industry is susceptible to forgery and human error, same with the medical industry. So, there can be faults like, mismanagement of data or like writing faulty prescriptions and false medical insurance claims. It can help solve these problems by cross-analyzing the data and alerting when precarious medical practices take place.

Considering all the above applications in the medical field and many other industries who use data science extensively, one can say today there is a huge need to train data science professionals and that is why one should think of earning a data science certification.

Source by Shalini M