Data Integrity and Analysis in Healthcare

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Table of Contents

Summary
Quality Measures Overview
Graph Analysis and Mock Data Interpretation
References

Summary

Data collection is an effective method to understand the root cause of the problem and identify an appropriate solution. In the previous paper, data integrity in healthcare was a proposed Best Practice response to the tragedy that occurred due to organizational failure. However, conducting a preliminary analysis of the problem and developing a comprehensive spreadsheet of relevant metrics is essential. In the current paper, structural, process, and outcome measures act as quality indicators of the organizational environment in the facility. Ultimately, data analysis plays a vital part in the improvement of the healthcare practice.

Quality Measures Overview

Structural, process, and outcome measures are essential metrics for any improvement in the healthcare industry. This framework was developed by Avedis Donabedian to create a complex approach to resolving problems in the workplace (Lorini et al., 2018). The structural domain concerns organizational issues, process measures refer to activities by the practitioners, and the outcome domain evaluates the changes (Lorini et al., 2018). According to the research by Watson Health (2021), vast emphasis on quality metrics and consequent response separates the best hospitals from the medium-performing facilities. Namely, the authors have found that more than 70,000 additional lives could be saved in hospitals in the US if all hospitals achieved the same quality standards (Watson Health, 2021). Based on the overview of the quality metrics, the current paper proposes a spreadsheet and a chart on data collection, presented in Table 1 and Figure 1, respectively.

Response to Organizational Failure in the Workplace (Mock Data)
Process Measure	Data Collection Method	Data Analysis Method	Current Level	Desired Level	Outcome Measure
The percentage of medical errors (among all treatments)	Medical Records/Direct Observation	Statistical/Factor Analysis	10%	3%	Reduction of medical errors based on the human factor
Risk-adjusted complications index (30 days)	Health Surveys	Descriptive Analysis	15%	12%	Reduction of medical complications among patients
Risk-adjusted readmissions index (30 days)	Hospital Internal Data	Statistical/Factor Analysis	15%	10%	Reduction of readmissions
Medical equipment utilization	Hospital Internal Data/Direct Observation	Descriptive Analysis	60%	85%	Improved medical equipment utilization rates
Communication Optimization	Direct Observation	Descriptive Analysis	50%	85%	Optimized communication
Structural Measure	Data Collection Method	Data Analysis Method	Current Level	Desired Level	Outcome Measure
Availability of real-time data access	Hospital Internal Data	Report	0	1	Real-time data would allow identifying the patient’s health history and code status immediately in the rehab unit
Availability of effective interpersonal communication methods	Staff Surveys	Descriptive Analysis	0	1	Better communication implies a faster response to the problems
Management of staff expertise	Hospital Internal Data	Report	0	1	Comprehensive management of personnel expertise would mitigate the risks of medical errors due to the lack of competencies

Table 1: Response to Organizational Failure in the Workplace

Graph Analysis and Mock Data Interpretation

The primary data collection methods are direct observation, medical records, internal facility data, and surveys, depending on the type of quality metrics. For instance, conducting surveys is an optimal choice to identify any potential health complications of patients in the 30-days period after release from the hospital. The proposed measures in Table 1 directly relate to the issue of organizational failure, which was suggested as the primary root cause of the tragedy. Consequently, all metrics are necessary to identify the most relevant problematic points of the facility, such as the lack of high-quality communication and organizational structure. Lastly, the mock data presented in Table 1 and Figure 1 could be interpreted as the potential outcome of the suggested response. For instance, better communication and data accessibility would drastically reduce the number of medical mistakes and improve the quality of healthcare services. Ultimately, the current paper has demonstrated the potential impact of the proposed Best Practice solution by applying the data analysis approach.

References

Lorini, C., Porchia, B. R., Pieralli, F., & Bonaccorsi, G. (2018). Process, structural, and outcome quality indicators of nutritional care in nursing homes: A systematic review. BMC Health Services Research, 18(1), 1-14.

Watson Health. (2021). 100 top hospitals(28^th edition).

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Data Integrity and Analysis in Healthcare

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