Healthcare is now opening up for individuals with an IT background. This article by CIO magazine covers the main players in the #HIT space, their roles/responsibilities, and how much they’re getting paid to do so. Check it out!
Data analytics may be the key to improving operating efficiency, contain costs, and ensure quality care. The adoption of business intelligence and data analytics has been commonly accepted by CIO’s in a variety of industries, but the healthcare industry is still far behind in the acceptance of data analytics. Hospitals generally lack the appropriate resources needed to help turn clinical data into insight. The use of advanced data analytics in US hospitals was only at a 10 per cent adoption rate in 2011.
Healthcare is in focus for a massive rehaul. This is a good summary of the current issues that can be addressed from the insight of analytics. Solutions derived from analytics business decisions are effective in saving cost and providing patient centric care. Adoption of the technology though has been slow paced. Incentives in all forms are now changing the land scape. Read on to get a glimpse of Data Analytics and Health Care.
Data anaytics firm specializing in oncology sets out to give cancer doctors access to more meaningful data from a larger cancer patient population and make them more connected.
One of the biggest frustrations cancer doctors have is feeling like they’re the only ones treating cancer, according to Nathaniel Turner, co-founder of cancer data analytics startup Flatiron Health. He and co-founder Zachary Weinberg are about to start the second of a two year pilot program to create a virtual tumor board of sorts by giving physicians access to more meaningful data from a larger patient population.
The long-term goal is to give them a better sense of the best treatment options for their cancer patients depending on the type of cancer they have. ..
Turner points out that only roughly 4 percent of cancer patients are in clinical trials. Its solution is designed to retrieve data from the remaining 96 percent and improve clinical trial participation. Data analytics in the context of cancer would seem a strangely specific place for a couple of advertising technology professionals to enter, albeit successful advertising professionals who sold their business Invite Media to Google in 2010. Turner and Weinberg are intimately familiar with the challenges cancer patients face from friends and family and are strongly motivated to use technology to knock down silos and improve data transparency for oncology professionals. Turner points out that advertising technology is practically stratospheric next to healthcare IT and believe they can advance the quality of technology. ...
The best thing about the Big Data hype in pharma is how effectively it's shed light on all of the Small Data problems the industry is facing. The roots of the Big Data movement in pharma were innocent enough: challenges in storage, data access, and data analytics that organizations started seeing with shifts toward high-throughput screening and massive genomics data sets. But as Big Data became more and more mainstream, the range of business challenges that got slapped with the "Big Data" label started ranging further and further afield. Industry analysts noticed this quickly, redefining Big Data in terms of the three (or four) Vs--not just volume but also variety, velocity, and variability. Others have been quick to follow. At a recent conference on data-driven drug development, speaker after speaker stood up to talk about their approach to Big Data, and each speaker immediately qualified that they were speaking about the variety of data, rather than the volumeof data.ets, about relevant manufacturing or reimbursement concerns, etc. This is a typical "small data" problem. The information needed to form a complete understanding of the drug-development landscape is scattered across journal articles, grant and IP databases, regulatory filings, clinical trial results, and research presentations. Requirements also vary from one licensing opportunity to the next, meaning that there's no possibility to build a one-size-fits-all solution. The total data involved in this sort of competitive intelligence analysis may be relatively small--certainly no more than a few GB of data--but both the diversity of data and the value of this Small Data problem are enormous.
The most important data-related challenge facing pharma is to use data--any data--to make more and more critical business decisions. Most of these decisions don't need Big Data: they need the right data--whether Big or Small--and they need it at the right time.
There's a good reason for this. While it's true that voluminous Big Data problems are sexy and grab headlines easily with exotic talk of petabytes and exabytes, the number of people across a pharma company who actually deal with these volumes of information as part of their day-to-day job is vanishingly small. Put another way, while Big Data is a real problem, it's not a Big Problem. What is a Big Problem, on the other hand, is the challenge of dealing with the diverse variety of (small) data that's needed for decision-making throughout the drug discovery, development, and commercialization life cycles.
You might see analysts refer to this as the variety axis of Big Data, but the challenge is really around getting unified information access.
One aspect of this challenge that every pharma organization faces is in harmonizing data as it is aggregated. For example, any references to ALS, Lou Gehrig's disease, or amyotrophic lateral sclerosis need to be known as the same disease so that data about the disease from one source (e.g., pathway data) can then be integrated against other information from another source (e.g., affected population data).
Another aspect of this challenge is the extent of data diversity that faces pharma today. Any unified approach to data must take as broad an interpretation of relevant information as possible. That means information needs to include traditional structured data (e.g., pathway, target, and genomics databases, CDRs and CTMSs, or manufacturing, finance, and CRM systems), completely unstructured text content (e.g., trial protocol documents, in vivo assay write-ups, clinical case reports, or product perception in social media sites), and all sorts of semistructured sources in between (e.g., CRO-generated spreadsheet data or public NCBI XML data).
That kind of broad and deep view of data grants scientists, business analysts, safety officers, managers, directors, and executives access to the critical data that informs their decision-making, wherever the data may be. The process of harmonizing data may be internal, but the data itself may come from just about anywhere--CROs and CMOs, content vendors, even public data--and access needs to be timely. Decision makers can't afford to wait three months for an IT project to gain access to data needed for a decision due this week.
By allowing business users to get immediate and integrated access to all data relevant to critical business decisions, regardless of its location and format, pharma companies can gain a significant competitive advantage. For example, to maintain robust pipelines, Big Pharma continues to look for earlier- and earlier-stage drug candidates to license. But the earlier in development a compound is, the riskier a licensing deal can be. Mitigating this risk requires knowing as much about the candidate drug as possible: about its indication, about its mechanism of action, about competing products and development programs, about the IP landscape, about leading researchers in the area, about expected safety and efficacy targ
No matter how much technology we throw at it, the diabetes epidemic just won’t budge. Today, 8.3% of the U.S. population has the disease--a problem that cost the country $245 billion in 2012 alone.
For the past few years, drugmaker Sanofi US has run the $100,000 Data Design Diabeteschallenge, a call for entrants to design data-driven diabetes solutions. This isn’t a challenge for flash in the pan ideas that disappear soon after winning. Past competitions have yielded successful initiatives like Ginger.io, a behavioral health analytics startup that recently raised $6.5 million.
The finalists for this year’s competition (theme: using open data to make the right diabetes decisions at the right time) are below.
The GoCap is perhaps the simplest concept of the bunch: it’s a high-tech replacement cap for pre-filled insulin pens that can read dose amounts and time, and then wirelessly communicate that information to cell phones and glucometers. The resulting data can be used by both patients and large organizations for analysis.
CONNECT & COACH TM
A product of software development firm PHRQL, Connect & Coach TM calls itself the first clinical and consumer application to let dietitians and diabetes educators perform Diabetes Self-Management Education and Medical Nutrition Therapy in local communities. The product is designed for supermarket and pharmacy use.
Created by healthcare analytics company Allazo Health, the AllazoEngine attempts to solve the niggling problem of medication non-adherence by using existing data from its members to predict who will neglect to take their pills--and the best way to get them back on track.
Like many products breaking into the market today, Nuduro provides healthy meal recommendations that match customer lifestyle, taste, and nutritional requirements. Unlike the other products out there, however, Nuduro presumably focuses specifically on diabetes patients.
MEDISAPIEN DIABETIC CLINICAL DATA REPOSITORY
This product, created by ZyDoc, is an enterprise healthcare analytics platform that lets users deposit all sorts of unstructured data--dictation, legacy data, transcribed text, and more--and transforms it all into fully-coded structured data. The platform is obviously relevant outside of the diabetes world as well.