Tuesday, May 31, 2016

HIM Professionals Critical Skills: Data Retrieval, Analysis and Reporting



Currently in health care organizations electronic health records (EHRs) allow providers to collect, retrieve and report different types of health data.  Making sure that the data is accurate and complete is the responsibility of the health information management professional.  Data analysis is one of the core competencies of the HIM professional.  Understanding what the data is telling you is key to the HIM role.  HIM professionals must possess knowledge in three primary areas:


  • Health data capture and maintenance
  • Health Information analysis and output
  • Health information resource management and innovation


As providers continue to demand health information technology systems that can manipulate data in novel ways, HIM professionals must be ready and able to tap into that data and tell the stories behind it.  (Improving Data Collection across Health Care Systems, October 2014)


Data retrieval


Data retrieval is an increasingly complex task as EHRs and other new applications continue to churn out huge volumes of data across disparate sites of care.  HIM professionals must identify and track all data sources that feed into the enterprise-wide data warehouse.  An incomplete data inventory leads to incomplete analysis.  HIM professionals must also be able to migrate and integrate data from diverse internal and external sources. 


Disaster recovery is also an important component of data retrieval.  HIM professionals play a significant role in the development of a formal disaster plan.  HIM professionals must ensure that this plan included information about data backup, disaster recovery, emergency mode operations, testing and revision and applications and data critical analysis.  Testing of a disaster recovery plan should be done periodically. (AHIMA, 2011)


Data integrity


Data integrity is the foundation of HIM.  Without clean data, any analysis, reimbursement, and clinical decisions can’t possibly be accurate or informed.  HIM professionals must ensure that errors within the HER are corrected and that all source systems include corrections as well.  They must also closely monitor the Master Patient Index (MPI), looking for and correcting duplicates and other patient identity errors.  As technology (e.g. computerized physician order entry, EHRs and computer-assisted coding) is implemented, HIM must be able to ensure that this technology provides an accurate output of data and that users understand their role in terms of validation.


HIM professionals—can—and should also engage patients to improve data integrity.  Portals, when integrated with the HER, give patient access to their own health data.  Patients can partner with their health providers to validate their own health information.  (AHIMA, 2011)


Data analysis and reporting


Data analysis and reporting will only continue to increase as technology allows providers to capture new and critical information.  HIM professionals can help identify opportunities for the use of this data to improve business intelligence, clinical care and decision-making throughout the enterprise. (Data Analytics for Health Care)


HIM professionals must be prepared to design requirements, criteria, and metrics to meet requirements for analyses and interpretations.  These needs will vary by researcher, clinician, executive, payer, consumer, etc. 


When analyzing and reporting data, HIM professionals must also ask these questions:


  • From what source(s) was the data obtained?
  • Is the data accurate?
  • Is the data complete?
  • Does the data meet the end user’s need?


Conclusion


Identifying a list of critical skills for the HIM professional to possess is important for several reasons, the most important being that one of the goals of health information management is to determine whether patients are receiving quality care. This means measuring how well health professionals follow practice guidelines and procedures.  If an EHR is set up properly, reports generated from the information stored in it can illuminate which guidelines and professional practices lead to better outcomes.  If the EHR is not set-up properly, this type of reporting is not possible. Moreover, an improperly set-up EHR could lead to the generation of inaccurate results, redundancy of data, wasted time and effort, and perpetuation of medical errors.  Acquisition of this skill set (data retrieval, analysis and reporting) will distinguish HIM professionals from their counterparts in the health care profession and contribute to the goal of quality care.  (Hanks, 2015)   


Frank M. Valier, D.B.A.                                                                                                                                               
Assistant Director, Health Information Management                                                            


 


References


AHIMA. "HIM Functions in Healthcare Quality and Patient Safety. Appendix C: HIM’s Role in Data Capture, Validation, and Maintenance." Journal of AHIMA 82, no.8 (Aug 2011):


Ikanow: Data Analytics for Healthcare: Creating Understanding from Big Data.http://​info.​ikanow.​com/​Portals/​163225/​docs/​data-analytics-for-healthcare.​pdf,   


Improving Data Collection across the Health Care System.  October 2014. Agency for Healthcare Research and Quality, Rockville, MD.  http://www.ahrq.gov/research/findings/final-reports/iomracerport/reldata5.html


Hanks, G. “Importance of Data Retrieval and Analysis in Health Care.” Journal of AHIMA 86, no.2 (Feb 2015


Raghupathi W, Raghupathi V: An Overview of Health Analytics. 2013, working paper


 


 


 


 


 


 


 


 


 


 

Wednesday, February 24, 2016

HOT TOPIC IN HEALTH IT: ENTERPRISE DATA WAREHOUSE

Hello HIM Students!  Most of you know that February is Information Governance month. In recognition,  Frank Valier, Assistant Director of HIM and the advisor to many of you, has written an article to give you some more insight into a related topic.  Enjoy!

An Up and Coming Practice in Healthcare IT: Enterprise Data Warehouse
Healthcare has been slow to recognize the value that data and analytics could have on both the clinical and the business sides of the house – the hospital and integrated delivery network, or the physician practice when compared with other industries – financial, retail and manufacturing for example. “Health care systems are facing multiple pressures now that really make analytics move from a nice-to-have to really, truly an imperative for survival in the coming years.” Brett Davis, Deloitte consulting principal and general manager of ConvergeHEALTH told Healthcare IT News in a recent interview. 
Once, all things data related revolved around the EMR and it held a prime position at the center of a healthcare organization’s universe.  Now the EMR is just one source of collected data supporting a particular kind of medical work.  By itself it is insufficient for the sophisticated data analytics the healthcare industry needs today (Pal, 2016).  
 
If you’re going to achieve high performance analytics, the EMR alone won’t cut it.  You need an enterprise data warehouse (EDW).  In fact, there is no viable alternative to an EDW if you want to successfully use analytics to improve the cost and quality of care.  By incorporating the EMR’s and other systems’ data into an EDW, you create a database that enables health systems, accountable care organizations (ACOs), physician groups and others to predict and manage patient care and improve cost and quality (Big Data and the Future of Healthcare, 2015).  
 
Essentially, the utility of an enterprise data warehouse (EDW) that saves a copy of data collected from all internal sources as well as external registries is limitless.  Reports generated and the addition of graphical formats, eye-catching charts, and comparisons allows teams across the healthcare organization to see and prioritize areas with the greatest opportunities for improving outcomes.  Everyone from clinicians to analysts benefits from an enterprise data warehouse.  Highlighting specific measures for improvement, and EDW helps an organization critically evaluate not only care processes for chronic diseases but procedure-specific processes as well (Lamble, 2015).
As a baseline the following characteristics should comprise any well-designed EDW:
 
Dependent on Multiple Source Systems.  An enterprise data warehouse (EDW) is populated by at least two source systems.  Examples include EHRs, billing systems, registration systems and scheduling systems (Lamble, 2015).
Cross-Organizational Analysis.  An enterprise data warehouse is designed specifically to enable data analysis across business and clinical processes – that is, the ability to analyze and link data across multiple source systems that support various business processes.  For example, a EDW could enable the analysis of data from an HER coded in SNOMED and data from a billing system coded in ICD by aggregating the key elements required for the analysis from each system, regardless of the terminology used (Lamble).
Trends, Metrics, and Reports.  An enterprise data warehouse helps identify trends and previously unknown relationships in business processes.  The data output is characterized by metrics and reports (Lamble).
Large.    Enterprise data warehouses in today’s information intensive organizations may contain billions or records constituting dozens and even hundreds of terabytes of data (Lamble).
Historical.  A data warehouse stores many years of data, typically at least five and sometimes as much as 30-years’ worth (Lamble).
 
Beyond these key, common components, there are structural differences and functional nuances that will turn the EDW your organization chooses into an indispensable tool.  Arming yourself up front with knowledge about the platforms, options, vendors and unique needs of the health care industry will help you ensure the EDW you select will lead you directly to success (Pal, 2016). 
 
Frank A. Valier, DBA
Assistant Director, Health Information Management Program
                                                                                                                                                               
 
References
 
Bergeron, Bryan (2013) Developing a Data Warehouse Guide for the Health Care Enterprise, Chicago, IL, HIMSS.
 
Big Data and the Future of Healthcare. (December 3, 2015).  Retrieved from http://www.computerworld.com/article/3011000/big-data/big-data-and-the-future-of-healthcare.html
 
Evans, R. Scott, Lloyd, James F., and Pierce, Lee A. (November 3, 2012) Clinical Use of Enterprise Data Warehouse.  Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540441/
 
Jiang, Bin (July 12, 2012) Redefining the Enterprise Data Warehouse by Requirements Categorization.  Retrieved from http://www.b-eye-network.com/view/16213
 
Kobielus, James (April 8, 2008) The Enterprise Data Warehouse, Defined, Redefined, Evolving with the Times . Retrieved from http://blogs.forrester.com/james_kobielus/08-04-08-enterprise_data_warehouse_edw_%E2%80%94_defined_refined_evolving_times
 
Lamble, Mike (March 9, 2015) Modern Enterprise Data Warehouses: What is under the Hood?  Retrieved from http://www.cio.com/article/2885055/data-warehousing/modern-enterprise-data-warehouses-what-s-under-the-hood.html
 
Pal, Kaushik (January 6, 2016) How Big Data Can Optimize IT Performance.  Retrieved from   https://www.techopedia.com/2/31538/trends/big-data/how-big-data-analytics-can-optimize-it-performance
 
Pal, Kaushik (December 5, 2015) Why Data Quality is Crucial to an Integrated Analytics Platform - A Health Care Example.  Retrieved from https://www.techopedia.com/2/31432/trends/big-data/why-data-quality-is-crucial-to-an-integrated-analytics-platform-a-health-care-example