Aggregate Data
Aggregate data is regarded as the numerical as well as non-numerical information collected for different sources on several variables. This type of data is compiled into summary report, which is primarily used for serving the public. Summary reports are primarily used for revealing observable information and insights when the elements of the collected data are looked into in isolation. Conversely, the summary reports can be used to examine trends and make particular comparisons. An example of aggregate data collected to determine a particular trend is the compilation and summary of information regarding the rate of graduating nurses in a particular school or state. Even though the identified data identified in the example is numerical it can be aggregate non-numerical information.
As applicable to the healthcare setting, aggregate data is used for developing information about a particular group of patients. The data makes it possible for healthcare professionals to take note of the common characteristics of the specific patient group, which can be used for the prediction of the course of their disease. Consequently, the data can provide information that can be used to determine the most appropriate way through which the particular condition could be treated. In addition to the identified application in the healthcare setting, aggregate data can be used for the prevention of a particular type or group of diseases (Ryan & Thompson, 2002). The data can be collected through different methods, which includes patient interviews and research. The collection and interpretation of this type of data provides healthcare professionals with the knowledge needed to handle different ailments, determine the medication requirements, and the progression of the condition in different settings.
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Some healthcare organizations keep data separately by using repositories. In such cases, the healthcare organizations should consider taking advantage of the beneficial elements attached to using aggregate data. The healthcare organizations should consider creating a comprehensive source system that will assist the personnel to access different insights that will enable the organization to gauge its performance on a macro-level (Ryan & Thompson, 2002). In light of the adherence to patient confidentiality standards, it is possible to question whether it is appropriate to use patient information to acquire aggregate data. However, it is vital to acknowledge that patient information is the primary source of aggregate data, which means that the collection of patient information requires their consent (Ryan & Thompson, 2002). This information is vital for the development and maintenance of a strong healthcare industry since the healthcare industry relies on evidence needed to create techniques that can assist in ensuring that healthcare is safer.
An example of the manner in which health record data from uniform data sets can be used includes communicating data from different clinicians to the next in a consistent way. By using common language during the communication process, it will be possible for the healthcare personnel to analyze the information in the aggregated data sets. The information derived would be used for the implementation of quality, patient safety, and the provision of information needed for policy decisions. The information is also used for healthcare policy research as well as for the investigation of the national health trends.
The clinical data dictionary contains numerous concepts, terms, and value sets that cover different conditions, clinical findings, and clinical procedures. It is possible to apply data dictionaries to improve a healthcare organization’s communication across the continuum of care using aggregate data. According to Lee et al., (2010), the clinical data dictionary was designed in a manner that can provide common healthcare language for the healthcare professionals, which could be used to index, store, retrieve, as well as aggregate clinical data across different healthcare specialties. The design of the clinical data dictionary also assists the healthcare professionals to access different sites that they could use for the provision of healthcare services. The professionals can utilize electronic medical records from different sources, consequently reducing the variability in terms of the manner in which the data is captured and encoded. The clinical data dictionary can then be used in research and for the clinical care of patients.
Comparative Data
The primary characteristic of comparative data is based on the provision that the collected data sets focus on making performance comparisons. Comparative data can be used in a healthcare setting to measure the quality of healthcare, which is considered as one of the fundamental concerns of healthcare providers in the contemporary society. According to Powell, Davies, and Thomson (2003), healthcare organization can use comparative data to assess the quality of care provided by quantitatively analyzing the routinely collected information. Based on the fundamental characteristic of comparative data, which involves the comparison of the performance of healthcare provision between healthcare providers, it is possible to highlight different issues that can be improved to ensure the delivery of quality healthcare. For instance, comparative data can be used to identify issues that include clinical performance, the identification of necessary improvement activities, and identify provisions that could be highlighted for further research (Powell, Davies, & Thomson, 2003). These provisions are necessary for developing suitable and effective research-based systems.
An example of a situation that might require the implementation of comparative data would be to assess the performance of a particular clinical process in one hospital compared to another. This comparison is only applicable when the two healthcare institutions are treating the same group of patients. One of the hospitals might be having a better survival rate after a given period compared to another. For this reason, the conclusion that can be derived from this comparison is that the hospital experiencing higher survival rates is applying higher quality clinical care compared to the other healthcare institution. The logic underlying the conclusion is based on the causality between the quality measures implemented in the two healthcare institutions. To determine the element that is causing the differences, the comparative data should be collected routinely, which is essential for determining the pitfalls experienced by the hospital with lower survival rates.
In terms of assessing the quality of a particular intervention, it is vital to use process measures that can illuminate definitive evidence needed for its provision. For this reason, Powell, Davies, and Thomson (2003) advocate for relying on the outcome measures of the intervention, which would only be beneficial if it provides the causal connection between the achieved outcome and the intervention implemented. This can only be achieved through the analysis of routine data. However, it is necessary to ensure that the data being used for the comparative analysis is accurate. This is the case when considering the application of sustainable interventions. On the other hand, the comparative data should be accurate because interpreting process measures is not a difficult task since the healthcare professional relies on the link that provides an appropriate remedial action.
As mentioned earlier, the clinical data dictionary the clinical data dictionary provides a common language that healthcare professionals use to index, store, retrieve, as well as aggregate clinical data across different healthcare specialties. Since clinical data is collected routinely, data tables can assist in categorizing different patient groups based on the variables being compared to come up with the most suitable intervention that could be used for the particular patient group. For this reason, the primary element to consider would include the availability and the consistency of the data acquired on the disease cases as well as trends from the sets of data being compared. The data table will also contain variables that focus on the data collected in relation to the applied interventions between the healthcare institutions. The inclusion of the variables and the disease trends will assist the healthcare professionals to conduct a performance measure, consequently coming up with the most suitable intervention for a particular set of variables that characterize the care interventions undertaken by the healthcare organizations. The data table standardizes case definitions, which is essential for ensuring that the epidemiological information at the primary and referral levels can be compared using the routine data collected from the patients.
Patient-Centric Data
Patient-centric data can be identified as health-related information gathered or collected from patients to assist in addressing a particular healthcare concern. The collection of the information about the patient is not limited to the individual patient, but the information can be obtained from family members or caregivers. One of the characteristics of patient-centric data is the notion that the patient determines his or her own health well-being, which is a determining factor in treatment and operation policies. Patient-centric or patient-generated health data can be collected through portable technology. Healthcare facilities and the practices implemented by physicians use the patient-generated health data to strengthen their relationships with patients through the development of patient engagement (Park et al., 2018). To strengthen this engagement, patients can either provide their health information to physicians of the information can be collected electronically, which is to be used in a clinical setting. One of the most beneficial aspects of using this type of data is based on the idea that the information collected provides the healthcare professional or physician with important information regarding the health of the patient between medical visits.
The patient-centric data is primarily applicable to patients that require routing care, such as diabetic patients. Such patients do not only receive personalized care plans from their physicians, but they also benefit from medication reminders, whose adherence is tracked based on the plans and the medication provided. The need for continuous tracking is a derivative of the need to establish partnership among the patients, their families, and the healthcare practitioners to make sure that the decisions made respect the needs and the preferences of the patient (Park et al., 2018). For this reason, patient-centric care solicits the patients are educated and supported as they make decisions necessary for supporting their care. As mentioned earlier, patient-centric data is collected through wireless technologies. The technological applications are developed based on the patient interactions with a physician. The data collected from the technological applications is then used to provide personalized prescription of medical applications as well as other management tools that the patient can use.
Data collected using the patient-centric approach flows among the system participants, physicians, payers, labs, and the providers. The objective of the data flow is to come up with novel ways that could be used for organizing patient data, which informs the institution of personalized diagnosis and treatment protocols. The treatment protocols depend on the patient’s genetic, biochemical, and laboratory markers, including the patient’s medical history. One of the trends that emerge from using patient-centric data includes the idea that a clinician will be able to incorporate patient-generated health data into this or her workflows. For instance, when a hypertensive patient checks his or her blood pressure using the wireless blood pressure cuff, the clinician will receive the result in the patient’s chart. The clinician will then be in a suitable position to provide suitable recommendations to the patient. The other trend involves the implementation of population health-management measures by healthcare providers. This implementation is part of the healthcare providers’ value-based strategy for providing care. instead of using the logging data of a particular patient, healthcare providers can implement monitoring programs for different health metrics and patients. The data derived from the monitoring solutions can be used to support patient post-discharge requirements.
One of the beneficial aspects of using patient-centric data emanates from the idea that it enhances patient-centered care. This does not mean that thought and workflow processes of the clinicians will change. Instead, part of the patient-centric process will involve the lead to the transitioning of the public and the healthcare providers to focus on the immediate needs of a patient. The use of technology in patient-centered care creates information that the clinician can use, based on the assessment of different patterns such as the patient’s sleeping pattern and dietary patterns, among other considerations. An assessment of the identified patterns can be used to enhance the quality of the patient-physician interactions, consequently improving the patient’s state of health. In this case, the patient-generated health data can provide the physician with a holistic view of the quality of life and the health of the patient over a given period. Therefore, the patient’s adherence to the treatment plan in place will make it possible for the physician to provide timely interventions whenever necessary, consequently preventing a costly care episode (Park et al., 2018). However, some of the concerns for using patient-centric data include low health literacy levels, different barriers to access, as well as the limitation of patient-generated health data resulting from privacy concerns.
The application of patient-generated health data within the health information system follows the need to consider some of the variables that could be used to identify the most beneficial information for improving the health care process. Researchers can assist in the identification process, including the determination of the different types of consumer as well as the provider education needs. The data table can be used to take note of the different variables and processes, whose results can make the adoption and use of the patient-generated health data easier. In this case, healthcare researchers can be involved in finding new ways that could be used to integrate consumer health technology and the information of the patient into their day-to-day lives. The integration will also take place in the workflows of the healthcare provider.
References
Lee, M. K., Park, H., Min, Y. H., Kim, Y., Min, H. K., & Ham, S. W. (2010). Evaluation of the Clinical Data Dictionary. Healthcare Information Resources, 16 (2), 82 – 88.
Park, Y. R., Lee, Y., Kim, J. Y., Kim, H. R., Kim, W. S., & Lee, J. H. (2018). Managing Patient-Generated Health Data through Mobile Personal Health Records: Analysis of Usage Data. JMIR mHealth and uHealth, 6 (4), e89.
Powell, A. E., Davies, H. T. O., & Thomson, R. G. (2003). Using routine comparative data to assess the quality of health care: Understanding and avoiding common pitfalls. Quality and Safety in Health Care, 12, 122-128.
Ryan, S. A., & Thompson, C. B. (2002). The use of aggregate data for measuring practice improvement. Seminars for Nurse Managers, 10 (2), 90-94.
