jtbioinf.ppt
TRANSCRIPT
Jamie TothSenior Systems AnalystChildren’s Hospital of Pittsburgh of [email protected]
BIOINF 2117Framework to a Conversation about Data
Warehousing in Healthcare
Sample, Abbreviated Hospital Data Flow
Why does healthcare need data warehousing?● Allows for a ‘single source of truth’ which is
often a pain point for organizations.● Allows self-service for users, as opposed to
constant interaction with report writers because of its more GUI-like look and feel.
● Integrates metadata to transform data into actionable information.
● Allows for analytics, data mining, and other suave data topics!
● Most importantly, it allows for the conversion of data into information, and information into knowledge, and (hopefully) ultimately business improvement (for healthcare, this means saving people’s lives!)
● The future needs of the field will require datawarehousing and business intelligence to move forward.
Is that true?●Allows for a ‘single source of truth’ which is
often a pain point for organizations – but only if they invest the time in defining terms and investing in the data model construction, development, and upkeep.
●Allows self-service for users, as opposed to constant interaction with report writers because of its more GUI-like look and feel – only if the users are trained in the data of that organization, and EHR’s data structure – unless it is a smaller, more controlled web-based solution.
●Integrates metadata to transform data into actionable information. Only if defined by an organization, and revisited at appropriate intervals for QI initiatives.
Is that true?
● Allows for analytics, data mining, and other suave data topics! Only if the data is primed for that use before hand.
● Most importantly, it allows for the conversion of data into information, and information into knowledge, and (hopefully) ultimately business improvement (for healthcare, this means saving people’s lives!) If everyone invests in the solution, and has an understanding of how their role fits into the overall solution.
● The future needs of the field will require datawarehousing and business intelligence to move forward. Emphatically, Yes.
Single Source of Truth●What would seem to be the simple number
to achieve in healthcare is normally the hardest – such a ‘denominator data’ or data which represents a certain population.
●Often, different areas will define certain terms differently. For some regulatory purposes, a ‘patient’ may be census number, for finance, it might be the number of encounters (one regulatory patient can have many financial encounters), and for operations it could be the number of treatments (one patient on one encounter could have several treatments). Warehousing allows for all truths to be represented.
Data Maturity in Healthcare●Billing data is often the data with the most
focus placed upon it, and is often more ‘mature’ than clinical data.
●Clinicians are far more focused on patient care – especially in emergent and critical situations.
●Strong leadership must exist to support clear responsibilities and scope of care definitions for health care workers as it pertains to the health record.
Transactional Data Modeling●Aims for high table normalization●Focused on actions or verbs – orders,
debits, credits, plans, activity.●Tends to have more rows than columns by
design for fast transaction-based queries.
Data Warehouse (‘Dimensional’) Modeling●Aims for data summarization and capturing
of metadata information.●Attempts to express data in several
dimensions - such as how many cancer (dimension) patients (metric) came from 5 miles (dimension) around x facility (detail)?
●Often has more columns than rows, allowing for closer to natural language understanding of database terms.
Data Warehouse (‘Dimensional’) Modeling●OnLine Analytical Processing – allows for
the building of cubes from relational databases. This allows for the pivoting of data.●Look at procedures performed across months●Look at procedures performed across
diagnosis groups.●It allows us to look quickly and efficiently at
the data in dimensional slices.
ETL / ELT●Extract, Transform, and Load (or Extract,
Load, and Transform).●Allows for the application of business rules to
pure data to extract information.●Allows to clean out certain areas of data pain
– if systems were changed, or implemented in that time it will allow the capture of complex business rules.
●Allows for fast reporting on summarized information.
●Can create many points of information from very few, focused points of data, and expand as EHR implementation does.
EHR Vendors and Business Intelligence●Vendors are more concerned with
regulatory requirements across all of their clients (International) and than true Business Intelligence / dashboarding. Those that receive a majority of funding from international business will often even be weak at billing functions.
●Often will provide basic data models based on their ‘default’ or ‘best’ setup.●More often than not, this ‘best’ setup is not in
place at any of their larger, multi-clinic and multi-disciplinary clients, as customization and implementation of the EHR to fit clinical and operational needs does not allow for a vanilla set up!
EHR Vendors and Business Intelligence●Will rarely support growth or extension of
the model.●Some do not include summarizations as
part of the data model.●Weak at best documentation.
●I can’t stress this enough.●The most obvious impact is the inability for
the ‘common user’ to write their own report. ●If a robust EHR has not yet been fully
implemented for some time, reporting will be crippled.
BI Vendors and Healthcare●Vendors are concerned with growing a
vertical and rounding out their list of clients.
●Often can hide missing sections of Business Intelligence in the reporting layer (missing metadata can be programmed around).
●May not be sensitive to the organization’s politics.
●Often reluctant to allow for user data entry areas to reflect clinically abstracted information, or other customizable lists.
●Are often not as well versed in regulations around research and HIPAA, as their specialization is reports, and not the regulatory processes around them.
Data Modeling
Data Modeling
Data Modeling
Business Objects and interaction with metadata
The Future●Health Information Exchanges (HIE)
●Large scale information sharing among hospitals● California Nursing Outcomes● Pediatric Health Information System● NHS Scotland Radiation Data Exchange
●Regulatory and Reform● Physician Quality Reporting Initiative● Meaningful Use● Pay for Performance
The Challenges● All challenges applied to the EHR are also
applicable to data warehousing●Including but not limited to:
● weak or missing leadership and championship● staff unwillingness to change● lack of checks and balances to ensure adherence to
workflow ● dynamic environment for regulatory requirements● uniqueness of Service Lines● lack of adequate time given to analysis of large scale
projects● scope creep.
● Often by the time a need for a long term warehouse is acknowledged, the past business rules may not have been fully captured.
The Challenges●Every area of healthcare requires a
different knowledge and skill set. ●Billing, Finance, Operations, Anesthesiology,
Research, Cardiology, Emergency Care, Critical Care, Pulmonology – all of these areas would express different needs and different views of data.
●Healthcare attempts to learn. This means that often, the questions that are asked of a warehouse or reporting team are not as specific as necessary.
●There are many barriers in communication between those that understand technology and those that have been trained to deliver healthcare.
The Challenges●Legacy data does not always support the
granularity of current user needs ●Certain areas may not be interfaced, which
will require double work on the part of the department and increased points of failure.
●Often ‘simple’ outcomes metrics (such as mortality) are difficult to glean from data in the best of cases.
●Some changes in the organization’s use of the EHR may not be fully documented, resulting in a longer development time for ETL or reports.
The Challenges●The more individual facilities that have data
contained in a warehouse that does not incorporate business rules for a particular organization (such as systems only configured by a vendor), the more difficult it becomes to write reports.
●Often clients do not know what their reports need to be when selecting and configuring an EHR, and resolving the issue takes a high degree of cross-functional communication and understanding .
The Challenges● Developers often do not understand the need
for user-entered data, which is often central for healthcare administrative functions such as quality, infection control, joint commission, and other regulatory reporting.
● Some key business rules owner are reluctant to share their knowledge in order to protect their sense of job security.
● During initial development, it is rare that all data stakeholders are included.
● Figuring out who showed up somewhere is not hard. Figuring out if they were already there (bed census)? A lot harder. Why? Transactional data and business intelligence tools don’t always work well together.
