Friday, December 21, 2012

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



Tuesday, December 4, 2012

Click here for article: mHealth Q&A
Picture courtesy of http://healthinformatics.wikispaces.com/mHealth

Monday, December 3, 2012

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



Friday, November 30, 2012

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

Sunday, November 18, 2012

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

Wednesday, November 14, 2012

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