Big Data. Big Decisions
 InformationWeek
Special Coverage Series

Commentary

See more from this author

Healthcare Tech: Can BI Help Save The System?

Initiatives like nationwide, integrated e-medical records won't happen until we get beyond closed, proprietary architectures. Business intelligence is a solid place to start.

By Boris Evelson InformationWeek
August 20, 2009 08:00 AM

Healthcare IT is a good place to be these days. While IT budgets in many verticals have been tightly reined, healthcare is enjoying multiple government mandates. This has resulted in an infusion of funds to modernize and integrate IT infrastructure, applications, and data.

However, we aren't starting from a high ground. There are multiple challenges to attaining a 21st century-grade IT environment. Among them:

  • Errors abound: Information management systems and computerized physician order entry (CPOE) applications accounted for a staggering 84% of the 43,372 computer-related medication error records in a 2006 study of the United States Pharmacopeia MEDMARX database.

  • Proprietary, closed architectures still rule: Hospital information management applications are often based on hierarchical databases that don't speak common query languages like SQL or MDX--the basis for all modern business intelligence tools. Even worse, some of these applications aren't architected with separate data and application logic tiers.

  • No data transparency: Applications with proprietary, hidden data models don't allow for plug-and-play interfaces with standard data integration technologies like ETL (extract, transform, load) and CDC (change data capture). This environment encourages ex-developers of these proprietary, closed applications to take advantage of their inside knowledge of how these apps work to make a living building custom interfaces for clients.

  • Incomplete standards: Data exchange standards like HL7 only work for about 80% of the content (and that's for administrative data, it's even less so for clinical data). The rest must be custom integrated every time.

  • Huge chunks of master data management are missing: MDM, a key to effective BI applications, works mostly for patient information and maybe billing codes, but not for anything else, like drugs (good luck trying to find a standard code for 200mg ibuprofen gel coated caplets), conditions, and treatments (there's no such thing as a "standard treatment" for a particular ailment--it's all subjective). For example, one senior healthcare IT manager tells me that glucose tests are coded differently in every single lab system she looked at, so her team spent countless hours coding mapping tables.

  • The world is vendor, not user, centric: True, most of the state-of-the-art (i.e. proprietary) healthcare applications are very powerful and function rich, but few vendors seem to care about integrating with other vendors' applications.

    •  1 | 2 | 3  | Next Page »
    , join the conversation


    Related Reading

    News

    Commentary

    Most Popular

    On the Web

    Slideshow

    Video

    More Insights

    White Papers

    More >>

    Webcasts

    More >>


    Currently we allow the following HTML tags in comments:

    Single tags

    These tags can be used alone and don't need an ending tag.

    <br> Defines a single line break

    <hr> Defines a horizontal line

    Matching tags

    These require an ending tag - e.g. <i>italic text</i>

    <a> Defines an anchor

    <b> Defines bold text

    <big> Defines big text

    <blockquote> Defines a long quotation

    <caption> Defines a table caption

    <cite> Defines a citation

    <code> Defines computer code text

    <em> Defines emphasized text

    <fieldset> Defines a border around elements in a form

    <h1> This is heading 1

    <h2> This is heading 2

    <h3> This is heading 3

    <h4> This is heading 4

    <h5> This is heading 5

    <h6> This is heading 6

    <i> Defines italic text

    <p> Defines a paragraph

    <pre> Defines preformatted text

    <q> Defines a short quotation

    <samp> Defines sample computer code text

    <small> Defines small text

    <span> Defines a section in a document

    <s> Defines strikethrough text

    <strike> Defines strikethrough text

    <strong> Defines strong text

    <sub> Defines subscripted text

    <sup> Defines superscripted text

    <u> Defines underlined text

    BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

    Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

    Follow InformationWeek

    By The Numbers

    What Are Your Primary Concerns About Using Big Data Software?

    Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
    Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

    What Do You Think?

    What's your attitude about SQL analysis on top of Hadoop?
    We want fast, standard SQL analysis capabilities on Hadoop ASAP
    Hadoop is for unstructured data; SQL is for relational databases
    We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
    Given strong SQL support on Hadoop, we'd nix the data warehouse
    We're not interested in Hadoop
    No opinion



    Related Content

    From Our Sponsor

    Five Big Data Challenges and How to Overcome Them with Visual Analytics

    Five Big Data Challenges and How to Overcome Them with Visual Analytics

    Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

    Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

    Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

    Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

    Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

    Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

    High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

    Big Data: Lessons from the Leaders

    Big Data: Lessons from the Leaders

    Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

    Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

    Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

    Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

    Informationweek Reports

    Research: The Big Data Management Challenge

    Research: The Big Data Management Challenge

    The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.