How Medical Data Acquisitions Can Assist Hospitals
In the modern world data is the new currency and means power. However, since we are flooded with information it is becoming more and more difficult to select the relevant parts and discard the rest. Automated systems could be the key to collect meaningful data, organize it in the right way and prepare it to be analyzed to extract trends that dictate further action.
Challenges and solutions
Russ Staheli, a healthcare data analyst discussed the 4 meaningful ways to make full use of available data:
- providing your data analysts with an enterprise data warehouse, in order to avoid manual manipulation of data which can make it prone to errors;
- give analysts a safe testing environment to play around with copies of the data without destroying the original recordings;
- don’t just rely on business intelligence tools and allow your senior analysts to build their own models, not just use standard tests;
- provide direction and cross-functional communication to help analysts understand the medical phenomenon behind the numbers and test meaningful correlations.
Data Acquisition Can Reduce Costs
The medical systems are always under financial pressure and any means of cutting costs is more than welcome. DAQ (Data Acquisitions) systems reduce the labor intensive work previously performed by nurses to record each patient’s response to treatment or stimuli. These systems connect sensors to a DAQ device which processes signals and sends them to a computer. Although much more accurate and cheaper than nurses, these systems pose concerns over data security, as described by a study:
“Recent federal regulatory guidelines intended to reduce the administrative costs and burdens associated with health care mandate the standardization and facilitation of electronic transmission of medical data. With the concurrent electronic movement of health information comes concerns over patient privacy and security concerns.
Healthcare managers are thus tasked with identifying management policy or guidelines aimed at establishing a management structure for healthcare delivery in a data-driven, evidence-based system. A required part of this structure involves the identification and implementation of prescribed data management strategies, now mandated by federal law. “
DAQ to Help Find New Cures
Scientists need medical data to develop models, to assess treatment response and to evaluate drug efficiency. In order to be statistically relevant, the number or recordings should be large, starting at 30, but depending on the population under study, can get as high as 5000. It becomes clear that not all data can be collected sometimes from the same medical unit and that information needs to be aggregated from different sources.
The challenge in this case is that each unit will use their own collection systems and information will not always be congruent with the other sources, either in formatting, deepness or even accuracy. It becomes clear that a standard is needed.
A Serbian study on lab systems pin-pointed exactly this problem, together with another non-technical one:
“The first and foremost problem that anyone developing Laboratory Information System will encounter is the difference in communication protocols on equipment from different vendors. Some vendors have specialized software that can be installed on the computer […] and use it to program the biochemical analyzer. This software is very expensive and often very hard to integrate into the existing information systems. Another question is whether or not to disturb the existing methodology and workflow of laboratory personnel by introducing new centralized device programming.”
It follows that before investing in such systems it is advisable to find those with the most widely used and user-friendly interfaces to allow medical staff to record data with ease, without disturbing daily medical routines. One possible cost-conscious and accessible solution is provided by DAQ Systems, which offers a wireless device compatible with common routers, thus eliminating the need of special interfaces. Also these devices can act as hubs, collecting info from a wide range of sensors, diminishing the need for a DAQ/ sensor, thus saving even more money.