Patient Data In The Age of Technology

Effective data management provides a practical way to improve the performance of healthcare systems. Professionals in the healthcare sector can identify and correct problem areas in various ways. They need to collect, conduct a thorough analysis, interpret and respond to specific performance measures.

To learn more, checkout the infographic below created by Duquesne University’s Online Doctor of Nursing Practice degree program.

nursing patient data infographic

Add This Infographic to Your Site


<p style="clear:both;margin-bottom:20px;"><a href="http://onlinenursing.duq.edu/blog/patient-data-age-technology/"><img src="http://s3.amazonaws.com/utep-uploads/wp-content/uploads/duq/2017/05/17161613/nursing-patient-data-infographic.jpg" alt="nursing patient data infographic" style="max-width:100%;" /></a></p><p style="clear:both;margin-bottom:20px;"><a href="Online DNP program" target="_blank"></a></p>
	

Effective data management provides a practical way to improve the performance of healthcare systems. Professionals in the healthcare sector can identify and correct problem areas in various ways. They need to collect, conduct a thorough analysis, interpret and respond to specific performance measures.

 It comes as no surprise that data collection tools are increasingly becoming popular in healthcare. The tools make it easier for professionals to analyze data by aggregating from multiple sources. The information is channeled into a single, integrated data repository. When data is being collected in the analytic format, organizations can leverage large volumes of data. The approach eliminates the possibility that some key data may be missing.

 In many cases, the data can be captured in a discreet field. However, implementing changes to the system takes time. Some organizations may find it difficult to collect data in an electronic health record (EHR). The practical solution for this issue is to enter data into an Excel spreadsheet or one-off Access database.

 On the downside, these one-off solutions may cause a number of problems, such as affect data quality, collaboration and security. One-off databases and spreadmarts are known to create quality issues, particularly when maintained separately from the source system. When it comes to collaboration, using a self-created data collection tool like an Excel spreadsheet creates new data siloes.

 The siloes present new challenges because they are not ideal for collaborating, particularly in relation to versioning. This makes it difficult for multiple users to work on the spreadsheet without distorting the information. The issue can be resolved by restricting editing to one person at a time. However, the approach may not fit everyone’s schedule.

 On the other hand, data security is another major concern when using one-off solutions and spreadmarts. Collaborating on spreadsheets has the capacity to present security risks. This is true even when working on encrypted laptops since the spreadsheets will be shared via email or other channels.

Big Data Analytics In Healthcare

Big analytics in healthcare is undergoing phenomenal changes. It provides a viable way for professionals in the sector to gain valuable insights from large data sets, which helps reduce costs and improve outcomes. The majority of medical data is stored in the hard copy but organizations are digitizing the information.

 Reports state that the healthcare system in the United States generated up to 150 exabytes of data in 2011. Experts say the big data in the country will reach the zettabyte (1021 gigabytes) scale before hitting the yottabyte (1024 gigabytes) mark.

 The data plays a critical role in the delivery of healthcare services. Some of the key functions that leverage the information include disease surveillance, clinical decision support and population health management.

 Big data incorporates healthcare data sets that are highly complex and large. The information is difficult to manage using conventional systems. The data management process is complicated by the speed at which the information must be processed and the diversity of data types.

 The information consists of patient data, social media posts and machine-generated data. It also includes administrative data, such as pharmacy, medical imaging, insurance, laboratory data and more.

Technological Advancements That Impact Data Processing In Healthcare

Technological innovation continues to transform the way healthcare organizations handle data. A wide variety of devices and systems are being introduced with the aim to boost productivity and efficiency.

 The electronic health record (EHR) enjoys widespread use since its inception. By 2013, 80 percent of healthcare facilities had switched to EHRs. Prior to the introduction of the solution, organizations relied on disparate systems. The changes enabled hospitals and other facilities to achieve an integrated and efficient care for patients. As a result, patient data is more centralized and accessible. Experts say EHR can be used as a data and population health tool.

 Reports state that over 80 the majority of physicians are leveraging EHRs. The systems are commonly used to record patient demographic information. Many doctors are taking advantage of advanced functions like viewing imaging results. In addition, the systems can handle computerized prescription order entry, view laboratory results, record clinical notes, problem lists and patient medications.

 Primary care doctors boast the highest rate of adoption of certified electronic health records. The rate of adoption for this category stands at 79 percent. More than half of these physicians are using all basic functionalities when compared to surgical practitioners. Meanwhile, solo practitioners have been linked to the lowest rate of EHR adoption.

Planning For Data Collection

Areas where improvement is necessary determine the data collection strategy. To achieve this objective, professionals must identify the best source of data that provides valid answers to the evaluation questions. Some of the possible sources include clinical observations, people and records.

 The methodology for collecting information may entail surveys, record analysis, focus groups, literature reviews and more. Registries make it easier to track quality measures. In some cases, organizations do not need to collect all the available data. Evaluating a subset can be sufficient, particularly when dealing with large volumes of data.

 Collecting comprehensive data is necessary when using QI reports to generate snapshots or profiles and manage provider performance measures. The available resources and logistical program considerations determine the structure of data capture timeline.

 Quality measures can be created using wide-ranging techniques, such as counts, proportions, medians, ratios and means. The ideal method is influenced by the selected quality measures and the evaluation questions involved. The most commonly used method to construct quality measures entails proportions with denominator and numerator.

 The proportion’s denominator shows the total number of patients eligible or facing the risk of a numerator event. Professionals can find endorsed quality measures on the National Quality Forum (NQF) website.

Role Senior Nurse Informaticists

The role of senior clinical informaticist has become a key position in medical facilities. Its rise to prominence is fueled by an array of healthcare reform-related initiatives, which are either voluntary or mandatory. Senior informaticists play an integral role in the implementation of information technology infrastructure as well as optimization and integration of IT systems. The activities are aimed at enhancing clinical decision support and point-of-care data collection.

Share This!