Healthcare Data Processing

Automated processing of Medical Reports

• Extracting information from various disparate systems (patient care management, coding, billing and accounts receivable, clinical test and treatment and others)
• Analyzing data from a wide spectrum of diverse reports such as Registration, Clinical Patient Care, Coding/Financial, Patient Follow-up, Operations, etc.
• Converting information from all of these systems via existing reports into live data to be manipulated and reused in formats that meet the user’s needs
• Creating unified views, reports and interactive portals

Getting you back to patient care

Suppose a healthcare organization has 50 users who average about 12 hours per month extracting data from existing reports to create the reports they need. TextConverter will bring down the development time by 90% saving about 10 hours per month per user. Now, let us calculate the total savings for the entire organization from employing TextConverter 4.0 for your data processing operations:

50 users X 10 hours per month X $40 an hour X 12 months = $240,000 of savings per year.

Many individuals spend even more time re-entering data to produce their reports, but with TextConverter’s new user interface they all will be self-sufficient in extracting the data they need in a fraction of the time and with 100% accuracy. The time they’ve been spending on reporting can be recaptured to allow them more time to focus on patient care.

Does your current solution do all this?

• Data extraction from any input format TXT, PDF, RPT, HTML, Excel, Word, XML, etc.

• Single and multi-line parsing templates, hierarchical templates, multi-level headers and footers

• Positional, floating, tag-based, and delimited fields

• Regular expressions for templates and field setup customization

• Interactive visual feedback, input-output synchronization. What you see is what you get.

• Input and output filters on the file, template and field levels.

• Lookup tables, regular expressions, and scripting

• Prebuilt data extraction from composite input fields: addresses, names, phone, SSN, etc.

• Calculated fields

• Script-based customization

• Duplicate records suppression

• Self-auditing

• Summary, detailed, and detail with subtotals reports

• Tables, crosstabs, Line and Bar graphs, Pie charts, 3D graphs and more

• Full control of data extraction application to fully automate processes where necessary

The TextConverter functionality below is intended to describe the major methods expected to be used for the vast majority of data extraction projects. There are many other features and options that can be used in combination with these in order to precisely define extraction parameters.

TextConverter has two primary template roles that will recognize and extract data from the two most common report formats that exist.

Hierarchical - In the hierarchical mode sophisticated algorithms are used to capture data from the selected levels above and below the detail data to populate the fields based upon the levels' positions relative to the detail data.

Flat Mode - In flat mode all fields of each detailed record will be populated with the closest qualified value from each nondetail level.

Scripting - Scripted data extraction is also available where preprocessing of the data is wanted in order to collect and combine data into records where the logic for doing so is not solely defined by the document structure and/or to perform calculations for processing the data to prepare it prior to export. Scripting can be used in conjunction with TextConverter's other modes, and it can also be used as the only extraction method within a project if necessary.

TextConverter has three primary methods of extraction from the individual report lines.

Fixed - A fixed extraction assumes that all of the fields being extracted at a particular level always begin at the same positions and occupy the same maximum number of characters.

Tag Based - Tag based extraction simply utilizes specific labels within the report to capture data at a certain proximity relative to the label text.

Manually Defined - A manually defined extraction begins with the line being parsed using logic that considers field delimiters. This can be adjusted by customizing delimiters, adding or removing fields and adding regular expressions to define patterns to use to capture field content.

Healthcare Solutions

Department Exam/Procedure Charge Reconciliation - Extract exam/procedure information from clinical department reports and charge data from account charges in billing system to verify that charges have been generated for all items. This has been used to capture significant revenue that would have otherwise been lost.

Blood Utilization Review - Combines three components (blood units given, hemoglobin levels and surgical procedures performed) to systematically determine whether blood transfusions are appropriate to ensure better patient care.

Employee Time Analysis - Data from timekeeping system reports is extracted each day and distributed to department to keep employees aware of potential overtime to be avoided. The data is also used to track other issues, like excessive tardiness or absences.

Employee Work Lists - Data is extracted from existing reports to be resorted and segregated into lists to be given to individual employees for the work they are to perform throughout their shifts. Respiratory Therapy patient treatments is an example of this.

Infection Control Organism Tracking Lists - Various reports are generated to track various microorganisms that Infection Control is required to monitor and report. These reports are scheduled to be automatically generated each day. This allows for easy analysis of the data, while providing a paperless archive system for initiatives that require this kind of reporting.

Pharmacy Antibiotics/Organisms Review - Prescribed antibiotic information is combined with detected organisms to help confirm most appropriate antibiotics, if any, to use. The logic in this report provides pharmacists with a ready-to-use document rather them having them spend extended amounts of time processing the information for this report themselves.

Laboratory Logs - Information for countless initiatives that require the use of information from complex laboratory log reports is easily collected. The most common example of this is turnaround times for certain kinds of testing required to be within certain parameters.

Department Monthly Operating Indicators - Information that department managers/directors use on a routine basis is extracted from a corporate report repository and parsed for distribution for the use of individual department managers/directors.