Real-life case studies of Holcomb Reporting Solution’s approach in action. Click on each to learn more.
Create centralized, automated KPI reports from several sources and various types of data
Client Profile: Large Regional Healthcare System
Problem: Creating a report with key metrics across different teams requires gathering several different reports from different sources and manually converting and combining elements into a new report. This manual process is inefficient and delays team members’ ability to analyze the results to make more informed decisions.
Solution and Benefits: A two-page pre-formatted deliverable for each team that provides key metrics for profitability and operations. These deliverables are created all within minutes and allows for more informed decisions to be made faster. This optimization was facilitated by a custom-made Python program that can be run centrally, allowing the user to choose:
1) the time period
2) for what team(s)
The program either consumes pre-existing reports from each team or queries the data directly from the appropriate databases. The program automatically:
1) combines and transforms the data into organized results
2) organized results are placed in the pre-formatted deliverable
Automate recurring reconciliation reporting
Client Profile: Large Regional Healthcare System
Problem: Creating a report to help reconcile important records between two systems is a tedious, recurring activity that often must be completed manually. This takes time every month, which is used to enhance productivity elsewhere.
Solution and Benefits: A reconciliation report that only includes items that did not reconcile between the two systems created in minutes. This allows team members can focus their efforts only on items that did not reconcile, freeing up time for other work.
This quickly created reconciliation report is the result of a custom-made Python program that ingests the records from both systems, matches pre-selected criteria, and returns only the items from each system where a match was not found.
Create dynamic forecasting models using project-specific trends
Client Profile: Large Regional Healthcare System
Problem: Forecasting revenue and expenses for large projects that span multiple years is complex and requires the creation of ad hoc spreadsheet models each time revenue and expenses change, which can be frequent throughout the duration of a project.
Solution and Benefits: A forecasting model that allows for an efficient, scalable approach to forecasting a portfolio of projects. Features of the model are:
1) a macro-enabled workbook that automates the forecasting of expenses and revenue over the entire term of a project
2) utilizes past expense and revenue trends to forecast into the future, but these trend assumptions can be overridden based on judgement and updated circumstances.
3 As actual revenue and expenses become known, the data supporting the model can be easily updated and the model will dynamically adjust the forecast time periods and any default trend assumptions.
Generate productivity and revenue modeling for staffing needs
Client Profile: Large Regional Healthcare System
Problem: Determining the impact to revenue and expenses when hiring an additional revenue-generating employee is complicated. A newly hired employee can directly bring in revenue to offset their cost of employment as well as indirectly by supporting other employees as they focus on other revenue-generating activities.
Modeling this staffing situation can be time consuming and can be more accurate when you consider a range of potential outcomes, based on changes to assumed inputs.
Solution and Benefits: A model that illustrates the estimated direct and indirect revenue, cost of employment, and the resulting impact on income of adding a revenue-generating. Attributes of the model include:
1) A custom-made Python program that uses a spreadsheet as an interface to ingest user inputs, either known or assumed.
2) The inputs along with prior productivity data are used to calculate estimated direct and indirect revenue, cost of employment, and the resulting impact on income.
3) These calculated results are pasted into a spreadsheet along with a table that shows how the resulting income would differ based on a change in certain inputs.
4) The productivity data used in the program can be easily refreshed and the model can ingest inputs and create outputs in minutes rather than hours.
Quickly highlight cost variances and identify key drivers
Client Profile: Large Nonprofit Health Plan
Problem: Creating a report that both illustrates the variance in costs between two different periods, and provides details for some of the primary drivers, requires pulling data from a series of canned reports, a database, or even both.
The manual creation of this report takes time each instance that it is needed, plus any additional research into observed variances can’t begin until the report is created and reviewed.
Solution and Benefits: A report that outlines dollar and volume variances that is separated by chosen categories. The report also provides some per unit metrics and a summary of top drivers of a variance.
Creation of the report is driven by a custom-made Python program that intakes data from existing reports or pulls directly from a database. The program allows the user to:
1) choose what categories should be included in the report
2) what periods should be compared
This type of report can be generated in minutes rather than hours and allows the focus to be on analyzing the observed variances to take a needed course of action.
Swiftly create a trend report and identify inconsistencies
Client Profile: Large Nonprofit Health Plan
Problem: Creating a trend report of revenue or expenses over time involves several manual steps. First, you need to upload data to a database or combine it with existing data. Next, carry out some data transformations. Finally, create a report that shows the amounts for multiple time periods.
The creation of the report is necessary to find any errors in the current or prior period. Only after errors are identified can corrective action be taken.
Solution and benefits: A trend report that displays revenue or expenses over a specified period of time and identifies specific amounts in the current periods or earlier periods that breach set thresholds for variance tolerances.
The report is the product of a custom Python program that:
1) ingests a canned report or directly queries a database based on a set of criteria selected (i.e., time frame, types of revenue or expense, thresholds, etc.)
2) creates a table by period of the different types of revenue or expenses selected for the report.
3) exports the table into a pre-formatted spreadsheet that is ready for a user to easily review
This reports can be created in minutes rather than hours and allows any corrective action to be identified and instituted faster.
Ensure alignment and verification between large amounts of complex payment data
Client Profile: Large Nonprofit Health Plan
Problem: A client needing to ensure payments made to customers align with terms of their contract can require manual review of the payments made, compared to original contracts. This step is crucial and must be completed before any remedy can be pursued for incorrect payments. This can be especially problematic when the methodology of payment and terms of the contract are complex, and the volume of payments is high.
Solution and Benefits: A report built from a custom-made Python program that matches detailed payment data (i.e., customer, type of product, service, and payment rate) with the terms of the contract.
The terms of the contract can be sourced either from a data repository or translated from the contract itself into a set of data elements that are inputs into a spreadsheet. The detailed payment data is sourced from a database or canned report. The program can handle a large volume of payments and can be updated to handle most complex situations.
Automate distribution of reporting deliverables
Client Profile: Large Nonprofit Health Plan
Problem: Delivering final reports to customers requires sending reports to various emails, servers or portals, depending on client preference and constraints. It takes time and well-documented steps to ensure reports are delivered to the correct spot, and these types of processes are often prone to user error.
Solution and benefits: A custom-made Python program that copies files from their stored location to other internal folder locations, or setup files for transfer to an external facing portal or older on a secure FTP site. This program reduces human errors and ensures final deliverables arrive on-time and in the client’s preferred location.
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