What's the Big Deal About Big Data?

My career has allowed me to learn quite a few things.  I have worked with software, testing it to ensure it met the requirement needs of clients. I have worked at a small cell phone company, trying to create a small and primitive data warehouse.  Now I work assisting in gaining adherence to ITIL methods for the entire government of a country.  These have all been great positions, but I have to say my first job out of college was by far my favourite.

At AIG, I was able to work with some very brilliant minds on the executive team, engage on some fraud reporting and saw my reports used throughout the United States.  I had a great manager, and awesome co-workers   However, by far the best part was working with data, reporting and writing SQL.  I loved it.  If you go back into my past a little further, you would see me asking my professor, after my class was finished for the semester, could I retain access to the database so that I could practice SQL more (can you say NERD!).

Nowadays my use of SQL is very limited and I miss it.  I miss reporting, seeing trends in data, and creating tools that guide decision making that can change the outcomes of the business and how everything interacts to create success.  So you should not be surprised that this term I have been hearing brings about a level of excitement for me.  It is Big Data.

OK, so I am sure you have heard of big data.  Maybe you know exactly what it is.  I am hoping to shed some light on this for people that have heard what it is, but have not had the time to look into it further.  Also I would like to convey why it will be important to healthcare.

What is Big Data?

When I was at AIG we had a data warehouse that had, most likely, millions and millions of records.  It was massive.  Big data is even bigger than that.  According to Wikipedia, big data is data sets so large that using standard database tools or data processing becomes very difficult.  The reason data is growing so much is because it is “increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, radio-frequency identification readers, and wireless sensor networks."  Wikipedia goes on to say that, "The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 quintillion (2.5×1018) bytes of data is created."

Wow, that is big!

This data is being used for data analytics, business intelligence and effective decision making.  Cliff Saran of ComputerWeekly.com alludes to a fact.  The bigger your data, the larger the gap grows in competitiveness that lies between you and your competitors.

With these advantages, come some challenges.  The biggest, ironically, is the size of the data.  Saran says that "big data will cause traditional practices to fail, no matter how aggressively information managers address dimensions beyond volume."  Another challenge is trying to understand how to use unstructured formats, such as text and video (McDonnell, 2011).  According to McDonnell, some other challenges include storage of this data and getting the most important data to the right people at the right time.  And of course there are going to be immense challenges when it comes to security and privacy.

Big Data in Healthcare

Is big data important to healthcare?  Irfan Khan (2012) says that if we estimate that each of our cells contains 1.5 GB of data, each one of us is walking around with approximately 150 zettabytes of data.  Thanks 150 billion terabytes.  So, it seems, big data is already in health care.  We just need to capture this data. By capturing this data there is a great potential for improved healthcare "including personalization of care, defining patient populations with a greater level of granularity, analysing unstructured data, mining claims data for insights that can improve wellness and patient compliance, advancing medical research, and helping governmental agencies detect fraud, identify best care delivery practices, and improve bio-surveillance (Terry, 2012)."

Big data has big possibilities, including better healthcare outcomes.  It also has big challenges, such as storage, privacy and how to work with new and unstructured data, to name a few.  The benefits outweigh the challenges, however.  mHealth, EHR and all of the technologies related to health informatics will continue to grow big data.  The big question is, are you ready?

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Khan, I.  (2012 November 15).  Where's the big data in healthcare IT? Look in the mirror.  Retrieved from http://www.itworld.com/big-data/315298/where-s-big-data-healthcare-it-look-mirror

McDonnell, S.  (2011 June 21).  Big Data Challenges and Opportunities.  Retrieved from http://spotfire.tibco.com/blog/?p=6793

Saran, C. (n.d.).  What is big data and how can it be used to gain competitive advantage?  Retrieved from http://www.computerweekly.com/feature/What-is-big-data-and-how-can-it-be-used-to-gain-competitive-advantage

Terry, K.  (2012 October 15). Health IT Execs Urged To Promote Big Data.  Retrieved from  http://www.informationweek.com/healthcare/clinical-systems/health-it-execs-urged-to-promote-big-dat/240009034

Wikipedia.  (2012 December 23).  Big Data.  Retrieved from http://en.wikipedia.org/wiki/Big_data

Is cloud computing safe for healthcare?

So I am learning today one of the pitfalls of this new [old] technology called Cloud computing.  AWS, the cloud service from Amazon, has gone down.  How did I notice?  It took Netfilx down with it, meaning I am missing out on my Star Trek marathon I had planned for Christmas Eve.  So this has me thinking.  How can we trust cloud computing?  Can we trust it with healthcare, where there are so many life or death decisions made everyday?  What ways can be implemented to ensure uptime if using cloud computing in healthcare?  Any thoughts?

Link: Netflix Down!!

mHealth's Emergence

Not too long ago I thought I had the best technology in the world.  Someone could call my number and this little gadget on my hip would beep.  I could then call them back.  As I look back at those days in nostalgia, I also think that this was also the latest in "mobile health" technology.  In an emergency a doctor could be reached at any time and respond to the needs of his or her patients.  Now days if we need to contact our doctor the latest emerging trend is mobile health or mHealth.  Messages, images, prescriptions, notes and all sorts of medical information are now traded on mobile phones and tablets.  mHealth is posed to change the way we interact with our providers, view our medical history and try to "self-diagnose" ourselves.  But this is not all that mHealth is bringing us.  We can also track our weight, create workout programs and monitor how we eat.  Diabetics can have their blood sugar automatically sent to their doctors, who in turn can create health plans to keep these people on track.

This all sounds like it is going to make life easier for all of us, right.  Well for certain groups of people these advancements are far from easy.  These people are the politicians and regulatory groups that must enforce safe and secure transfer of data.  People selling applications must also sell the products in a harsh economical environment, and find a way to convince consumers that it is worth paying for some of these pieces of software.  There are also researchers who must conduct studies to see how these technologies will fit in an ever-changing social environment.  And last but not least, the people that must build the infrastructure to support this massive flow of information.  Lets take a look at some of these complexities and see how mHealth will move forward.

Politics, Regulations and Your Mobile Freedom

Ponemon Institute, a research organization that conducts studies on privacy, data protection and information security policies, found that 94% of healthcare organizations have had at least one data breach in the past two years (SIW Editorial Staff, 2012).  This number is scary.  The most common types of breaches include lost equipment, employee errors, third-party error, criminal attack and technology glitches (SIW Editorial Staff, 2012).  Now with the rise of mHealth, there is a new avenue for private data to be leaked to people who should not have access.  Yes we are entering a world of more mobile access, but this access comes with a price.  This price is the potential for data to be stolen, lost or mishandled.  Eric Wicklund (2012), editor at mHIMSS, learned from Peter Tippett of Verizon Enterprise Solutions that "healthcare is 'dead last' among industries in using cloud computing and IT" and that "part of the reason for that is regulatory overhang."  Technology is advancing and healthcare is just entering the game.  There are many regulations and laws to be created to make sure data is safe as it flies over the airways.  Also with Bring Your Own Device (BYOD) it is becoming difficult for CIOs to find a happy medium between company policy and the law (Comstock, 2012). 

mHealth Faces the Economy

The economy may be a barrier to mHealth adoption, especially in developing nations.  Dan Jellinek (2012), states that one barrier is lack of investment funds because of the worldwide economic crisis.  Another is the misalignment of funding incentives (Jellinek, 2012).  He advises that governments around the world take these into account when planning infrastructure and policy (Jellinek, 2012). 

How mHealth will affect the Socio-Economic Climate

There is no doubt that this emerging technology will make an impact on how healthcare is delivered.  Studies are already being done on how far the impact will be.  The Boston Consulting Group (BCG) was commissioned to study these impacts.  These impacts will be especially felt in emerging economies.  BCG found that mHealth will provide the solutions needed in these economies and do it quickly (BCG, 2012).  They also found that mHealth will free up health resources by reducing paperwork, reducing human error, avoid duplication and reduce administrative burdens by 20 - 30 percent (BCG, 2012).  This will go a long way in countries that face short budgets and start to save and improve lives all over the globe.  Another example of the socio-economic effect is how mHealth will improve mother and child health.  Voice of America (2012) (VOA) gives the scenario of a pregnant woman whose mobile device sends messages timed to her pregnancy that tells her what to do and when to do certain things.  It will even alert her when to get special treatment, or prevention care (VOA, 2012). 

Building a Foundation

When using the technology to facilitate mHealth, there are a few challenges that need to be considered when building the infrastructure on which all platforms will live.  One consideration is the quality of connection and performance of the end device, according to Jenny Laurello (2011).  There must be a high quality of connections (Laurello, 2011).  Unfortunately in most cases connects on mobile devices are not as consistent as a desktop device (Laurello, 2011).  One way around this is to create a wireless network at the organization that can accommodate the increased load of all the mobile devices (Laurello, 2011). 

As you can see mHealth will make a great impact on healthcare of the future.  There are few barriers such as lack of oversight and regulation that can lead to data breaches, and key parts of the infrastructure must be upgraded, especially wireless technology like Wi-Fi.  There is also the lack of investment in a world recovering from a global economic catastrophe.  If these barriers can be over come the socio-economic affect will be saved and improved lives across the world and because of this the future of mHealth looks bright.

References:

Boston Consulting Group.  (2012 April).  The Socio-Economic Impact of Mobile Health.  Retrieved from http://telenor.com/wp-content/uploads/2012/05/BCG-Telenor-Mobile-Health-Report-May-20121.pdf

Comstock, J.  (2012 December 06).  BYOD, HIPAA are rock and hard place for CIOs.  mobihealthnews.  Retrieved from http://mobihealthnews.com/19385/byod-hipaa-are-rock-and-hard-place-for-cios/

Jellinek, D.  (2012).  A changing world.  Vodafone mHealth Solutions.  Downloaded from http://mhealth.vodafone.com/health_debate/insights_guides/politics_economics/index.jsp

Laurello, J.  (2011 June 28).  Network and infrastructure considerations for mHealth and mobile devices.  Retrieved from http://www.circadence.com/news/current/Network-and-infrastructure-considerations-for-mHealth-and-mobile-devices

SIW Editorial Staff.  (2012 December 10).  Study: Healthcare Data Breaches A Growing Concern.  SECURITYINFOWATCH.COM.  Retrieved from http://www.securityinfowatch.com/news/10840149/study-healthcare-data-breaches-a-growing-concern

Voice of America.  (2012 December 05).  Using a Mobile Phone to Improve Mother and Child Health.  Retrieved from http://learningenglish.voanews.com/content/wireless-phone-pregnancy-mama-baby-medical-mobile/1559325.html

Wicklung, E.  (2012 December 04).  Verizon's Tippett says mHealth data transfer and security must be invisible and seamless.  mHIMSS.  Retrieved from http://www.mhimss.org/news/verizons-tippett-says-mhealth-data-transfer-and-security-must-be-invisible-and-seamless

Click here for article: mHealth Q&A

Picture courtesy of http://healthinformatics.wikispaces.com/mHealth

Business Intelligence and Generating Revenue in Healthcare

Business intelligence and analytics have created an atmosphere of greater decision making for businesses for many years.  Using data from the many transactions that businesses perform has been one of the most valuable tools for businesses.  Business has created teams of people to crunch numbers and this is now aided with reporting tools like MS SQL Server Reporting Service and even simple spreadsheets and graphs generated in MS Excel.  Now that health care is moving towards capturing information electronically, how can these organizations leverage these types of tools to contribute to revenue generation?

FIrst, here is a quick definition of business intelligence (BI).  BI is, according to Margaret Rouse (2006), "a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions."  This means that BI encompasses the tools that gather the data, making sure that the data is clean and abides by a set of standards.  It is the databases and data warehouses that store the data and the use of SQL to retrieve the data.  BI practices are used to analyze the data by use of spreadsheets, pivot tables, graphs and charts.  The findings are they presented on presentations, accessed through portals that house dashboards, sent through emails and a host of other ways to present the data for decision making.

According to Paul Bradley and Jeff Kaplan (2010), when it comes to the financial information system's data, health care organizations can support the revenue cycle by targeting high value claims or accounts, identifying root causes of missed charges or bad debt, enhance staff productivity, and speed up resolution of bad debt.  These are all done by predictive analytics, which identify trends and use these to make predictions to head off issues before they can begin (Bradley & Kaplan, 2010).  

On the clinical side, by leveraging BI, hospitals can "provide an efficient means for clinical results reporting" according to Healthcare Financial Management Association (2008).  This efficiency can present data that shows and measures the efficiency and effectiveness of clinical processes (HFMA, 2008).  Also by opening up the systems to physicians to input various data, this can support the physician's own billing, which turn can improve coding (HFMA, 2008).

Ferranti, Langman, Tanaka, McCall & Ahmad (2010) point out that use of BI can help support revenue generation through clinical improvement in these ways:

1. Prevention of medical errors through use of enterprise data.

2. Using data to improve the business cycle.

3. Using health analytics for emerging health issues.

As you can see BI can help to increase revenue by use of data from both the clinical information systems and from the financial information systems.  By using predictive analytics, the organization can head off billing and bad debt before it becomes a problem.  Clinical data can improve outcomes, patient safety and look towards the future so that these organizations can make better decisions which will increase revenue in the long run.

References:

Bradley, P. & Kaplan, J.  (2010 February).  Turning hospital data into dollars.  Healthcare Financial Management, 64(2), pp. 64-68.  ISSN: 0735-0732

Ferranti, M., Langman, M. K., Tanaka, D., McCall, J. & Ahmad, A.  (2010).  Bridging the gap: leveraging business intelligence tools in support of patient safety and financial effectiveness.  Journal of the American Medical Informatics Association, 17(2), pg. 136.  DOI: 10.1136/jamia.2009.002220

Healthcare Financial Management Association.  (2008 August).  Leveraging Business Intelligence for revenue improvement.  Healthcare Financial Management, 62(8), pg. 1.  ISSN: 0735-0732

Rouse, M. (2006 November).  Business Intelligence (BI).  SearchDataManagement.  Retrieved from http://searchdatamanagement.techtarget.com/definition/business-intelligence

How mHealth is changing Africa

Picture courtesy of CNN.com

DICOM and challenges it faces in the ever changing healthcare IT environment

In this writing I will explain the Digital Image Communications in Medicine (DICOM) standards. I will consider the current and proposed changes in informatics information infrastructure, the challenges they may have on DICOM standards and why.

DICOM is made up of a long list of standards. According to Merge Healthcare (n.d.), these standards are:

Conformance – These are statements written to allow a network administrator to plan or coordinate a network of DICOM applications.
Information Object Definitions and Service Class Specifications – This standard defines the types of services and information exchanged using DICOM.
Data Structures and Encoding & Data Dictionary – This is how commands and data should be encoded for interpretation.
Message Exchange – This is the standard by which two DICOM applications mutually agree upon the services they will perform over the network.
Network Communication Support for Message Exchange – How message will be exchanged using TCP/IP and OSI.
Common Media Storage Functions for Data Interchange – This is the DICOM model for storage of images on removable media.
Media Storage Application Profiles & Media Formats and Physical Media for Data Interchange – Specifications on physical storage media and details of the characteristics of various physical medium and media formats.
Grayscale Standard Display Function – Display function for display of grayscale images.
Security Profiles – Secure network transfers and secure media.
DICOM Content Mapping Resource – Defines templates and context groups used elsewhere in the standard.
Explanatory information – Consolidates informative information previously contained in other parts of the standard.
Web Access to DICOM Persistent Objects (WADO) – Specifies a web-based service for accessing and presenting DICOM persistent objects.

Proposed changes in the US health informatics infrastructure include the adoption of health information exchanges and this can present a problem with DICOM and multi-site use. According to Langer & Bartholmai (2011), one challenge of multi-site interoperability is “the magnitude of data produced by imaging systems and unstructured text reports.” They go on to say that this is because this giant amount of information “has made it very difficult to share results among sites until the wide availability of both broadband networks and universal protocols.” This is definitely and challenge to image transmission.

Another transmission challenge is the use of XML, which is utilized by HL7 V3.0. One example is caBIG, architecture (Langer & Bartholmai, 2011). Langer & Bartholmai mention that a “vast majority of PACS and imaging modalities will require intervening computers to broker the DICOM to caBIG translation.

Another challenge is storage. Steve Langer (2011) writes, “images from different vintage equipment will encompass different levels of the standard.” What this means is that there can be ambiguity in what data elements mean, based on time periods when it was created and the location of data elements if its not in a standard location (Langer, 2011).

There are several challenges that DICOM face. These deal with image transmission because of large data sets and lack of utilization between standards. Data storage can also be an issue because of legacy definitions.

References:

Langer, S. (2011 November 12). A Flexible Database Architecture for Mining DICOM Object: the DICOM Data Warehouse. Journal of Digital Imaging, 25, pp. 206-212. DOI: 10.1007/s10278-011-9434-6

Langer, S. & Bartholmai, B. (2011 February). Imaging Informatics: Challenges in Multi-site Imaging Trials. Journal of Digital Imaging, 24(1), pp. 151-159. DOI: 10.1007/s10278-010-9282-9

Merge Healthcare. (n.d.). The DICOM Standard. Retrieved from http://estore.merge.com/mergecom3/resources/dicom/java_doc/javadoc/doc-files/dicom_intro.html

Data Modeling: A Crash Course

The healthcare industry is making great steps towards using IT to provide better healthcare outcomes, increase patient safety and attempt to reduce costs.  Behind every one of these initiatives should be a well thought out IT system.  The backbones of these systems are databases.  But how does a HCO or software company start out creating this?  It all begins with system analysis and design and this cannot be done without data modelling.  Here is your crash course to data modelling and why it is important.

Data Modeling

In the data modelling process there are three items that must be completed for success.  These are Conceptual Data Models, Logical Data Models and Physical Data Models.  In this writing I will explain what data modelling is briefly, what each of these models are and why they are important to the development phase of a software system.  I will also be using an example of new patient check-in system for Dr. Model’s office that allows patients to enter demographic data upon arrival.

First, what is data modelling and why is it important?  Data modeling is, according to Margret Rouse (2010), “…the formalization and documentation of existing processes and events that occur during application software design and development.”  When data modelling analysts will use tools and techniques to translate the complexities of a system into easily understandable data-flows and processes.  These are used as the basis for construction of a database system or the re-engineering of one (Rouse, 2010).  The items that come out of this exercise show the data at various levels of granularity.  Also, by having well-documented models, stakeholders can eliminate errors before coding a new system (Rouse, 2010).

Conceptual Data Model (CDM)

This is the highest level of data modelling CDM is a vital first step when it comes to systems analysis and design.  Pete Stiglich (2010) explains that by creating a CDM  “key business entities or objects and their relationships are identified.”  For example at Dr. Model’s office, the current system of collecting information would be to let the patient fill out a form.  The designers now create a CDM, that shows the various entities that will be needed, such as a patient table, insurance company table, date table, and there could be others.  The model will then show which entities are related to one another.  So for example the patient table will have relationships to all the other tables.   It would be good to note that at this point this is a very high concept level showing what tables are related.  When creating the CDM, stakeholders will inevitably find many-to-many relationships, but these will be addressed in the logical data model (Stiglich, 2008).

Logical Data Model (LDM)

1keydata (n.d.) defines a LDM as a model that “describes the data in as much detail as possible, without regard to how they will be physical implemented in the database.”  In this model analysts will now start to identify the fields that will be in the tables and the relationships between them (1keydata, n.d.).  They will also identify primary & foreign keys, and begin the process of normalization.  As a side note normalization, as defined by Mike Chapple (n.d.), is “the process of efficiently organizing data in a database.”  Normalization is what will address the many-to-many relationships, and get rid of redundancies.  Going back to the application for Dr. Model, the analysts now include fields in the tables.  For example, the patient table will include patient first & last name, medical record number (primary key), patient sex, patient age and many other bits of demographic information.  

Physical Data Model (PDM)

Exforsys Inc. (2007) defines this data model as “the design of data while also taking into account both the constraints and facilities of a particular database management system.”  This is where analysts take the LDM and make sure that all the pieces of data are configured based on the environment.  To further explain, as in our Dr. Model case and the patient table, now we define the patient first name as a variable character type field (varchar) and state how many characters it can contain.  The age field will be defined as an integer data type.  Knowing this information from the PDM can be useful in estimating storage needs (Exforsys Inc, 2007).  Analysts must also take note that this model may be different based on the type of database management system that will be used (Oracle, MS SQL Server, MySQL, etc.) (1keydata, n.d.).

In conclusion the end result of an application is reliant on a strong data modelling process.  This process will build the foundation for the database and without it many issues can occur.  It starts off as a very high level CDM, fills in the blanks with the LDM and then makes sure that it will all fit in a neat package with the PDM.  Any HCO needs to rely on this modelling process so that it can maintain quality data for the foreseeable future.

References:

1keydata.  (n.d.).  Logical Data Model.  Retrieved from http://www.1keydata.com/datawarehousing/logical-data-model.html

1keydata.  (n.d.).  Physical Data Model.  Retrieved from http://www.1keydata.com/datawarehousing/physical-data-model.html

Chapple, M.  (n.d.).  Database Normalization Basics.  About.com.  Retrieved from http://databases.about.com/od/specificproducts/a/normalization.htm

Exforsys Inc.  (2007 April 23).  Physical Data Models.  Retrieved from http://www.exforsys.com/tutorials/data-modeling/physical-data-models.html

Rouse, M.  (2010 August).  Data modeling.  SearchDataManagement.  Retrieved from http://searchdatamanagement.techtarget.com/definition/data-modeling

Stiglich, P.  (2008 November).  So You Think You Don’t Need A Conceptual Data Model.  EIMInstitute.org, 2(7).  Retrieved from http://www.eiminstitute.org/library/eimi-archives/volume-2-issue-7-november-2008-edition/so-you-think-you-don2019t-need-a-conceptual-data-model

How important are metadata and data dictionaries?

How important are metadata and data dictionaries?

In data warehousing and data storage, metadata and the use of a data dictionary is extremely important.  Metadata in layman’s terms is basically data about data.  It explains how the data was created, when it was created and the type of data it is.  Staudt, Vaduva & Vetterli (n.d.) state in their paper that when working with the complexity of building, using and maintaining a data warehouse, metadata is indispensible, because it is used by other components or even directly by humans to achieve particular tasks (Staudt, Vaduva & Vetterli, n.d.).  

Metadata can be used in three different ways:

• Passively – documents the structure, development process and use of the data warehouse system.  (Staudt, Vaduva & Vetterli, n.d.)
• Actively – Used in data warehouse processes that are “metadata driven.”  (Staudt, Vaduva & Vetterli, n.d.)
• Semi-actively – Stored as static information to be read by other software components.  (Staudt, Vaduva & Vetterli, n.d.)

So as you can see, the use of metadata is important, not only to store information about the data, but it is also used in processes by the data warehouse and by other applications.  Metadata also improves on data quality by providing consistency, completeness, accuracy, timeliness and precision.  This is because it provides information on the creation time, and author of the data, the source and the meaning of the data when it was created. (Staudt, Vaduva & Vetterli, n.d.).  

Regarding the data dictionary, this reminds me of when I would query the database at a past position of mine.  Because there was no data dictionary, it was hard to manually decipher the relevance of the data that I was searching for and where it was stored.  Because of this, I did not always bring back the correct fields necessary to complete my work and this caused devalued use of time.  On AHIMA’s website Clark, Demster & Solberg (2012) prepared an article about the use of a data dictionaries and how they can be used to improve data quality. 

• Avoid inconsistent naming conventions
• Avoid inconsistent definitions
• Avoid varying lengths of fields
• Avoid varied element values (Clark, Demster & Solberg, 2012)

By using the data dictionary, there is a consistency created in the data, which in turn improves data quality.

In conclusion, both metadata and data dictionaries are vital to creating consistent data.  This data can be tracked and can be used to create interoperable processes between the data warehouse and other applications.  Without these, architects are taking a chance and increasing their opportunities for use of less quality data.

References:

AHIMA. (2012 January).  Managing a Data Dictionary. Journal of AHIMA 83(1),pp. 48-52.  Retrieved from http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_049331.hcsp?dDocName=bok1_049331

Staudt, M., Vaduva, A. & Vetterli, T.  (n.d.).  The Role of Metadata for Data Warehousing.  Retrieved from http://www.informatik.uni-jena.de/dbis/lehre/ss2005/sem_dwh/lit/SVV99.pdf

eHealth, Patient-Interaction and Data Quality

eHealth, Patient-Interaction and Data Quality

Facebook, Instagram, Twitter, Pinterest.  Have you ever heard of these?  Of course you have.  Over the last several years, consumer interaction with computers has soared.  People are now shopping, video chatting, expressing themselves, posting pictures and conducting business using computers, and this is just the tip of the iceberg in terms of how the computer is being used to interact with the world.  And the computer is not the only means.  Using smartphones, the masses can now do all of this on the go, creating a never-ending flow of information.  What is this information made of? Data!

It is only natural that the healthcare industry, now venturing into the IT world, would want to jump on the train to ride the information highway.  With all the human computer interactions going on, and in the lay community, it only makes sense to find a way to have patients interact with their own health data.

Hesse & Shneiderman (2007) wrote the following in the abstract of their article.

“New advances in eHealth are prompting developers to ask “what can people do?” How can eHealth take part in national goals for healthcare reform to empower relationships between healthcare professionals and patients, healthcare teams and families, and hospitals and communities to improve health equitably throughout the population?” (Hesse & Shneiderman, 2007)

The data that is used by patients and providers goes beyond the one-to-one relationship between them.  This is the value that the emerging area of eHealth brings.  The physician is a “microunit” of a larger systems, which includes the care delivery team (nurses, office staff, etc) and the patient is also a “microunit”, surrounded by family & friends, and the community (Hesse & Shneiderman, 2007).

One way that patients can help improve quality of data is by looking up information on their own, and through a provider-sponsored portal (Geissbühler, 2012).  This will help them in “assisting” the provider with pinpointing their issues, while having a reliable source of information. 

Another emerging trend are “patient-controlled health information exchanges” (Geissbühler, 2012).  These are ways that a patient can control access to their health information, federating documents from several providers or organizations and even provide their own contributions (Geissbühler, 2012). 

A third way patients can interface with their health information is by using their “’digital proxy’, a mobile, always-on, permanently connected, and context-aware device such as a smartphone.” (Geissbühler, 2012).  Also homes can be made intelligent, and they can be made aware of the needs of their inhabitants (Geissbühler, 2012). 

Some of this may seem science fiction, but this is the way the health industry is moving.  People are getting more comfortable with sharing information online in the social networking world.  The use of smartphones is proliferating and can be a valuable asset.  All of this information sharing, and allowing patients to control their own data will help to increase data quality on a whole.  This is good not only for the patient, but also for the providers and the communities that the patients live in.

References:

Geissbühler, A.  (2012 June 2012).  eHealth: easing the transition in healthcare.  Swiss Medical Weekly, 142.  doi:10.4414/smw.2012.13599

Hesse, B. W. & Shneiderman, B.  (2007 May).  eHealth research from the user’s perspective.  American Journal of Preventive Medicine, 32(5), pp. S97 – 103.  DOI: 10.1016/j.amepre.2007.01.019

Keeping Data Safe

In order to protect information systems and data, a best practice is for organizations to develop and maintain a data security program.  Examine the essential elements of a health care information security program and why each element identified is essential.  Be sure to examine both technology and human factors. 

Columbia, SC, October 30, 2012, “As many as 657,000 S.C. businesses had their tax information stolen in the massive security breach at the state Department of Revenue…” (Shain, A., 2012).  October 2012, “Hackers were able to breach more than 60 Barnes & Noble (BKS) stores, including locations in New York City, Miami, San Diego and Chicago, and obtain credit card information…” (Graziano, D., 2012).  Again in October of 2012 a Vermont credit union accidently threw away two backup tapes, which could affect up to 85,000 individuals (Walker, D., 2012).  October of 2012 was a busy month for data breaches.  The breaches highlighted above have nothing to do with the health care industry, but as the proliferation of EMRs, EHRs, PHRs, mHealth, wireless technology, electronic claims processing and HIEs continue, HCOs will need to remain vigilant in protecting PHI. 

In 2009 Clifton Phua, wrote an article about computer fraud and security.  In it he noted that 81% of security breaches were from malicious outsiders, 17% from malicious insiders and 2% from unintentional insiders (Phua, C., 2009).  This means that anyone housing sensitive data must take measures to lock those who would want to get into the systems out, and to make sure those who have appropriate access do not intentionally or unintentionally disseminate protected information.

There are several ways to assist in ensuring that data will not be breached.  The first is on the technology side.  These include firewalls, intrusion detectors and robust anti-virus protection (Phua, C., 2009).  Firewalls stop intruders from getting into your private network through security rules and other measures.  There are two types, software firewalls and hardware firewalls.  If by chance someone does get in an intrusion detector will send alerts and appropriate measures can be taken.  Lastly anti-virus will stop malicious software from getting on the network, creating back doors for intruders.

On the human side one, HCOs should implement data handling policies (Phua, C., 2009).  Some items in a policy like this could be:

·      Masking unneeded data, such as the first digits of a social security number
·      Shredding paper and physically destroying hard drives
·      Reviewing the what, where and how’s of data
·      Background checks on employees
·      Auditing employees access to data
·      Data encryption on laptops and other portable devices, to protect information in the case of theft (Phua, C., 2009)
·      Using strong passwords

And the list goes on.

Another check that can be put in place is the use of thin clients.  These are machines that access data in a client-server fashion.  The machines can also have copy and paste, USB drives and other ways to export data disabled (Phua, C., 2009).

In conclusion, when measures are taken to protect PHI, both the use of technology and ways to recuperate from human error must be taken into account.  Attacks can come from the inside as well as the outside and breaches can be intentional and unintentional.  A wise plan takes all of this into account and sets up a roadmap towards a more secure infrastructure.

References:

Graziano, D.  (2012 Oct 24).  HACKERS STEAL CREDIT CARD INFORMATION FROM 63 BARNES & NOBLE STORES.  Retrieved from http://bgr.com/2012/10/24/barnes-noble-security-breach-credit-card-information/

Phua, C.  (2009 Jan 01).  Protecting organisations from personal data breaches.  Computer fraud & security, 2009(1), 13 - 18.  DOI: 10.1016/S1361-3723(09)70011-9

Shain, A.  (2012 Nov 01).  Data security breach expands to 657,000 S.C. businesses.  Retrieved from http://www.mcclatchydc.com/2012/11/01/173313/data-security-breach-expands-to.html

Walker, D.  (2012 Oct 26).  Vermont credit union discards unencrypted data of 85,000.  Retrieved from http://www.scmagazine.com/vermont-credit-union-discards-unencrypted-data-of-85000/article/265522/

Intranets and Health Care

Intranets and extranets are specific variants of Internet technology. Examine intranets / extranets in HCOs, the benefit to health care delivery, implications to patient outcomes/safety, and any impact to health care costs.

“Information is a source of learning. But unless it is organized, processed, and available to the right people in a format for decision making, it is a burden, not a benefit.” (Pollard, W., n.d.)

Intranets and extranets are about getting information to the right people. In this writing I will explain what are intranets and extranets, how they are used and how they can benefit health care organizations (HCOs).

An intranet is a local network available to a certain set of people that have the rights to view it. These are usually within one organization. Bradley Mitchell states that these networks are used to facilitate communication between people or work groups and are accessed over technologies such as Ethernets or WiFi and use common protocols such as TCP/IP (Mitchell, B., n.d.). He goes on to explain that extranets are extensions of the intranet that allows “outsiders” access, through the use of special firewall rules (Mitchell, B., n.d.). Some people are not aware that they are using their company’s intranet as they are accessed through web browsers. SharePoint is one of Microsoft’s fastest growing intranet applications.

Just like in the business world the intranet is being used by HCOs to get the right information to the right people. It allows collaboration, fast and efficient communication and reliable availability. Ong, K. R., Polkowski, M., McLemore, G., Greaker, M. & Murray, M. (2001) did an case study on how an intranet was being used at Saint Vincent's Catholic Medical Centers (SVCMC). They found that SVCMC was using their intranet for “. medical knowledge resources, clinical practice guidelines, directions, patient education, online forms, phone directory, and discussion forums” (Ong, K. R. et al, 2001). As you can see this was a vital way to exchange pertinent information for their staff, administrators, executives and even patients. They also found out that the intranet was getting over 3,000 hits per day, with over 800 of those hits coming from unique visitors and 74% of those visitors visited more than once (Ong, K. R. et al, 2001). As you can see that once an intranet is up and running it becomes a vital part of a HCO.

One of the main benefits of an intranet/extranet is getting information to those who need it to make key decisions.This will definitely affect patient outcomes and safety, as information is readily available, easy to access and up-to-date. Another benefit is lowered costs. Harno, K., Paavola, T., Carlson, C. & Viikinkoski, P. (2000) did a study on the effectiveness of intranets in telemedicine. The found that by use of intranets, cost were lowered due to less spent on personnel costs, internal and external service charges, material expenses, travel costs and rentals (Harno, K., et al, 2000).

In conclusion we see that intranets/extranets get the information to the right people at the right time. This can improve patient outcomes, deliver information quickly and efficiently, and reduce certain costs.

References:
Harno, K., Paavola, T., Carlson, C. & Viikinkoski, P. (2000 Dec 1). Patient referral by telemedicine: effectiveness and cost analysis of an intranet system. Journal of Telemedicine and Telecare, 6(6), 320-329. DOI:10.1258/1357633001935996

Mitchell, B. (n.d.). intranet. Retrieved from http://compnetworking.about.com/cs/intranets/g/bldef_intranet.htm

Ong, K. R., Polkowski, M., McLemore, G., Greaker, M. & Murray, M. (2001 Jan-Mar). Building and growing a hospital intranet: a case study. Journal of Medical Internet Research, 3(1), E10. DOI: 10.2196/jmir.3.1.e10

ThinkExist.com. (n.d.). William Pollard Quotes. Retrieved from http://thinkexist.com/quotation/information_is_a_source_of_learning-but_unless_it/226524.html

Will PHRs survive?

If you would have stated those three terms (EHR, EMR or PHR) to me few weeks ago, I would have not even have begun to know the difference.  Actually, I just thought they were different names for the same thing. I know better now. I am also of the mind set that PHRs do not currently balance.  Brian Dolan (2011) wrote an article in which he listed 10 reasons why Google Health failed. They are:

1. Google Health was not fun or social
2. Google Health was not trustworthy
3. Google Health was too cumbersome
4. Google Health did not involve doctors
5. It was hard for Google to partner with insurance companies
6. It was too hard to overcome the current reimbursement barriers
7. Google Health was poorly marketed
8. Google Health received poor C-level support
9. Google Health had no advertising opportunity
10. Google Health was not useful to consumers (Dolan, B., 2011)

As you can see five out of 10 of these reasons was based on consumer use.  Even you stated in your previous post that you wished that a doctor could be involved, which coincides with number four.  I am not saying that all PHRs are like Google Health, but you can see how it is difficult to get these types of operations up and running.  EHRs and EMRs are all clinical based so they are faring well. As I stated in my original post, if PHRs could find some cohesiveness and then be integrated, there are a lot of opportunities. 

With the demise of Google Health, this does not mean the demise of PHRs.  I actually believe that this may be a key tool in the future.  The world is moving towards the mobile devices and so many transactions today are moving towards the on-line environment. It is only a matter of time, in my opinion, that "mobile health" will see a renewed emergence.  In 2008 a survey was dconducted on perceptions of HIEs and PHRs.  Patel, Dhopeshwarkar, Edwards, Barrón, Sparenborg & Kaushal  (2010), surveyed 117 respondents and a majority of them stated that they would use PHRs based on certain factors.

• 51% would use PHRs if being treated for a chronic illness
• 90% of the people would use PHRs from the group of respondents with some college education
• 84% of the respondents who had shared personal information over the Internet in the past would use PHRs (Patel, V. N. et al, 2010)

In conclusion, there were other factors, but this shows that as people are more educated, and as they use the Internet more for other daily transactions, this may increase the adoption of PHRs in the future.  Google Health may have failed, but maybe the timing for this was not right.  People are becoming more tech savvy and I am sure we will see a greater adoption of PHRs in the near future.

References:
Dolan, B.  (2011 June 27).  10 Reasons why Google Health failed.  Retrieved from http://mobihealthnews.com/11480/10-reasons-why-google-health-failed/

Patel, V. N., Dhopeshwarkar, R. V., Edwards, A., Barrón, Y., Sparenborg, J. & Kaudhal, R.  (2010 July 29).  Consumer Support for Health Information Exchange and Personal Health Records: A Regional Health Information Organization Survey.  Journal of Medical Systems, 36(3), 1043-1052.  DOI: 10.1007/s10916-010-9566-0