Challenges in the health sector
The health care system is not only confronted with financial and structural challenges, but above all, concrete data protection requirements are of central importance. We show you how to master these challenges.
IT and digitalisation are too often seen in Germany as pure cost drivers, without taking into account the revenue opportunities, potentials and benefits for the healthcare sector. We know the challenges in the healthcare sector and help you find the best IT solutions. We will be happy to advise you on your options, show you potentials and options for action and accompany you during implementation.
These and other customers already trust us:
Customers consistently confirm to us possible optimisation potentials in the double-digit percentage range on the basis of time and monetary key figures.
Your advantages in the healthcare sector:
Use the potential of digitalisation and optimise your processes with our analytics approaches.
Reduce planning uncertainties in liquidity management!
Analyses and forecasts:
Maintaining business and hospital operations through meaningful analyses and forecasts by networking and providing all relevant and necessary information.
Use of automated analysis functions and presentation of results on the basis of meaningful key figures.
Comprehensive ad-hoc analyses for faster operational planning
Make your billing process faster and more efficient!
Support for the analytical provision of information within planning, forecasting and simulation processes.
Targeted support options, forecasting functions and optimisation proposals for MDK audits
Increase the transparency of results and the quality of decisions!
Digital decision support:
Analysis of data in real time to identify correlations
Increased data quality:
Central data management and parallel access to data where and when it is needed
Take more time for the important tasks!
Holistic treatment processes through comprehensive data integration in a target system
Avoidance of redundant work steps:
Technology-supported documentation and data management for smooth information exchange
Together we can master your challenges!
Analytics approaches to predict and manage processes:
Our analytics approach to predict and control billing processes in a timely manner can help accelerate liquidation and optimise processes in conjunction with your hospital information systems. For example, MDK audits can be avoided or supported through targeted forecasting and optimisation.
Liquidity planning can be optimised using modern planning applications supplemented by data science. We have already implemented numerous planning projects based on IBM Planning Analytics with Watson and achieved excellent results. For the optional addition in terms of pattern recognition for better identification of dependencies, we rely on IBM's AI platform Cloud Pak for Data. For the final optional step in planning, the optimisation based on the given framework parameters, we are supported mathematically by IBM Decision Optimisation.
Analytics approaches to forecasting and decision-making:
Our analytics approach helps you to identify correlations in patient data and analyse them in real time. The technology thus offers you the possibility to retrieve all relevant data automatically in processed form. A key advantage is that critical patient data within your hospital information systems can be visualised in a dashboard and you can make informed decisions and react at an early stage.
Transparency comes from visualisation. Whether dashboards, standard reports, automatic distribution of important key figures: we have already had good experiences with the proven IBM Cognos Analytics solution in numerous projects.
Within the same hospital, several IT systems from different providers are often operated with partly limited interoperability and standardisation. The fragmentation of medical services prevents holistic treatment processes and hinders the creation of a functioning value chain/integrated care.
Analytics approaches to bring data together:
Our data integration approaches help you merge your data from numerous systems into one target system. Whether batch processing, real-time, or organised as a data hub, this lets you say goodbye to fragmentation and data silos and gain complete access to the use of all data.
For the consolidation of information in the company, it is essential to keep the data up-to-date, comprehensible and correct. The aspect of "data governance" and data quality must also be considered. In many of our projects, we use IBM Cloud Pak for Data with the modules "DataStage" (for data integration) and "Watson Knowledge Catalog" (for data governance) as the central platform for data integration. If real-time data exchange is planned, IBM Change Data Capture (CDC) or IBM App Connect Enterprise can be used as a supplement.
In hospitals, a lot of information must be accessible and expandable at many stations. A lack of process standardisation and decentralised data storage lead to media discontinuities and make parallel access by all responsible persons, independent of time and place, almost impossible. Moreover, data entered by hand considerably reduces data quality.
Analytics approaches for intelligent document and data management:
An intelligent search and text analysis platform based on AI offers the possibility to digitally archive, process and retrieve data. Through further services, the platform can be used as a basis for cognitive assistants or as a knowledge hub. Smooth and digitised document management ensures access to high-quality data where and when it is needed.
On the one hand, central data management can be understood as an analytical discipline, i.e. as the creation of a data warehouse/data lake in which all relevant facts are stored centrally, comprehensibly and quality-assured. In our projects, IBM Cloud Pak for Data System, for example, the module "Netezza Performance Server" has proven itself for this purpose.
On the other hand, the central storage of documents can be the central aspect, i.e. the introduction of an "Enterprise Content Management (ECM)", which is optionally supplemented by AI-supported search and analysis. For our projects in the ECM environment, we use IBM Cloud Pak for Automation, and the IBM Watson components for AI support.