doi: 10.56294/hl202293

ORIGINAL

The Role of Data Analytics in Enhancing Decision-Making Processes in Healthcare Management

El papel de la analítica de datos en la mejora de los procesos de toma de decisiones en la gestión sanitaria

Snehanshu Dey¹ , Chetan Kumar Sharma²

¹Siksha ‘O’ Anusandhan (Deemed to be University), Department of Psychiatry, IMS and SUM Hospital. Bhubaneswar, India.

²Noida International University, School of Sciences. Greater Noida, India.

Cite as: Dey S, Sharma CK. The Role of Data Analytics in Enhancing Decision-Making Processes in Healthcare Management. Health Leadership and Quality of Life. 2022; 1:93. https://doi.org/10.56294/hl202293

Submitted: 22-07-2022                   Revised: 10-10-2022                   Accepted: 04-12-2022                 Published: 05-12-2022

Editor: PhD. Prof. Neela Satheesh

ABSTRACT

Introduction: the Intro explains how data analytics is becoming increasingly widely used in the healthcare sector and how it can enhance decision-making processes. It also emphasizes the necessity for improved healthcare management decision-making, as healthcare systems are functioning under growing pressure and complexity.

Method: the pertinent literature was completely revised to carry out the goals of this study. It covered different uses of data analytics in healthcare management, such as predictive modeling and risk analysis, along with performance evaluation.

Results: the Results tab describes the study’s main findings. These include the vital role that data analytics plays in detecting patterns and trends, anticipating results, and maximizing performance in healthcare management.

Conclusions: overall, data artificial intelligence is an upcoming trend in healthcare that will not only improve our healthcare handling but will increase the chances of any probability of being a pandemic. It can revolutionize how healthcare is provided, allowing clinicians to make decisions based on evidence and organizations to foster a data-driven culture.

Keywords: Data Analytics; Analytics; Healthcare; Clinicians; Risk Analysis; Data-Driven Culture.

RESUMEN

Introducción: la Intro explica cómo la analítica de datos se está extendiendo cada vez más en el sector sanitario y cómo puede mejorar los procesos de toma de decisiones. También destaca la necesidad de mejorar la toma de decisiones en la gestión sanitaria, ya que los sistemas sanitarios funcionan bajo una presión y complejidad crecientes.

Método: se revisó por completo la bibliografía pertinente para llevar a cabo los objetivos de este estudio. Abarcó diferentes usos de la analítica de datos en la gestión sanitaria, como el modelado predictivo y el análisis de riesgos, junto con la evaluación del rendimiento.

Resultados: la pestaña Resultados describe las principales conclusiones del estudio. Entre ellas, el papel fundamental que desempeña la analítica de datos en la detección de patrones y tendencias, la anticipación de resultados y la maximización del rendimiento en la gestión sanitaria.

Conclusiones: en general, la inteligencia artificial de datos es una tendencia inminente en la atención sanitaria que no sólo mejorará nuestro manejo de la asistencia sanitaria, sino que aumentará las posibilidades de que se produzca una pandemia. Puede revolucionar la forma de prestar asistencia sanitaria, permitiendo a los clínicos tomar decisiones basadas en pruebas y a las organizaciones fomentar una cultura basada en los datos.

Palabras clave: Análisis de Datos; Analítica; Atención Sanitaria; Médicos Clínicos; Análisis de Riesgos; Cultura Basada en Datos.

INTRODUCTION

This article gives you an article into how data analytical services and data analytics help in enhancing decision making in healthcare management area. Data is generated constantly in health care from sources, including electronic health records, medical devices, health surveys and administrative records.⁽¹⁾ The majority of this data is unstructured and quite complex, making it very challenging for healthcare providers to manually extract this meaningful insights. Analytics uses AI, machine learning, and other technology to mine this data, uncovering patterns, trends, and correlations that might otherwise go unnoticed.⁽²⁾ This enables healthcare organizations to make data-based choices, which often leads to better patient outcomes. The second type of data analytics improves decision-making & care for patients.⁽³⁾ For example, doctors are analyzing electronic health records data to identify high-risk patients with various diseases and implement early intervention strategies to prevent symptoms or at least treat these patients proactively (Bose et al. 2019). This can be done with the assistance of data analytics, which might be used to determine patients’ risk for diabetes by analyzing their medical history and way of life.⁽⁴⁾ This allows healthcare providers to formulate tailored interventions for preventing or managing diabetes before the disease progresses.” As a result, patients receive timely care, leading to improved health outcomes. Data analytics not only enables healthcare organizations to positively influence patient care, it also contributes to controlling healthcare costs.⁽⁵⁾ Every day healthcare is increasing more expensive and organizations are reducing their needless usages with slow services. Data analytics helps immensely in analysing the cost trends for the healthcare industry so that areas of cost optimization can be highlighted.⁽⁶⁾ For instance: And more importantly, with the power of data analytics, health care providers can identify expensive tests and services that need not be performed, thus maximizing their savings. Additionally, through data analytics, one can recognize the patients with high return on investment, thus optimizing resource allocation, delivering cost-effective care. Furthermore, data analytics also help healthcare organizations in managing risks.⁽⁷⁾ In addition to such benefits, data analytics in addition helps set the operational efficiency in well being management. Using data from administrative records and supply chain systems, healthcare organizations can identify opportunities for process optimization that result in both cost savings and higher efficiency. Healthcare providers can manage their inventory more efficiently with data analytics, which can reduce waste and unnecessary costs.⁽⁸⁾ In addition, you can use data analytics to manage the workforce by revealing insights on staffing requirements and improving worker productivity. It can optimize the utilization of resources and economical for healthcare organizations. One of the important segments where the data analytics can be implemented is healthcare marketing/ patient engagement. An increasing number of digital channels means that healthcare organizations can now collect vast amounts of information about patients’ attributes, and their preferences and behaviors.⁽⁹⁾ Access to this data can subsequently be processed to develop focused marketing and engagement tactics that can improve patient acquisition and retention. For example, by leveraging insights from data, healthcare providers are able to identify which channels are the most effective for reaching their audience — and customize communications to reflect this. With technology improving and high volumes of data being produced, it is highly expected that data analytics in healthcare is going to grow even more.⁽¹⁰⁾ When organizations in the healthcare industry, rely on data analytics, it can empower the right tools to deduce valuable conclusions that benefit patients output, as well as the organization.

· There is an immense ignorance within the medical professionals about data analytics.

· At the same time, while data analytics could help, its widespread use and application in healthcare systems management have not been easy for some of the reasons that includes resistance, cost etc.

· Healthcare data are poor in quality and quantity, especially the one, which is retrieved from electronic health records, while data analytics are complex in accuracy and reliability.

· Data analytics using patient data leads to ethical issues including privacy and issues of consent that need to be addressed to maintain patient trust and confidentiality.

· Still emerging are performance metrics for data analytics in healthcare decision-making, and few if any guidelines exist to monitor and assess analytics performance (Bickel et al., 2020).

The remaining part of the research has the following chapters. Chapter 2 describes the recent works related to the research. Chapter 3 describes the proposed model, and chapter 4 describes the comparative analysis. Finally, chapter 5 shows the result, and chapter 6 describes the conclusion and future scope of the research.

METHOD

Liu, H. Y.,et,al. Inter-professional nursing education encourages collaboration between medical professionals to facilitate outcomes for patients . But when it is the developing of two pieces together is embryonic, there must be some immediate trust, is conducive to the efficient work between the people, this is called swift trust. Best practices in terms of interaction, including open communication and mutual respect, creating an environment for collaboration. Darlow, B.,et,al. Interprofessional education has been shown to positively influence attitudes, skills and career trajectories. These benefits include fostering a collaborative, patient-oriented culture, improving communication and teamwork skills, and expanding other career opportunities across the health system with interprofessional collaboration. Best, S.,et,al. Professional identity in interprofessional teams is the way in which individuals perceive themselves in terms of their roles in a team of professionals from different fields of medicine Interprofessional Collab & Comm: The Key Elements of Professional Identity, Patient Outcomes & Team Effectiveness - A Scoping Review Mink, J.,et,al. Interprofessional training wards aim to develop the competencies of health care professionals by fostering interprofessional collaboration and learning. The present quantitative longitudinal study investigates the longitudinal effects on interprofessional competencies via such wards. Bentley, M.,et,al. Interprofessional teamwork, Working with health care professionals of different disciplines to design and deliver comprehensive primary health care services. Gadde, H.,et,al. AI-Enhanced Data Warehousing is a process that leverages artificial intelligence to augment the capabilities of data warehouse management systems. Shahid, N.,et,al. Artificial neural networks (ANNs) have been widely used in many fields of healthcare organizations decision-making. Generative AIs can help with medical diagnosis, predicting patient outcomes, drug development, resource allocation and even forecasting healthcare trends. Secinaro, S.,et,al. Artificial intelligence (AI) usage is transforming healthcare by accelerating and improving diagnosis, predicting patient-based outcomes and planning treatment. Hariri, R. H.,et,al. Uncertainty of big data analytics Such as issues with data collection, storage, processing and analysis as well as the potential for errors and biases Endriyas, M.,et,al. Performance data has been a topic of discussion .

Table 1. Comparative analysis of existing models
Author	Year	Advantage	Limitation
Vassakis, K.,et,al.	2018	Improved decision-making through more profound insights and patterns found in vast datasets.	One limitation of Big Data analytics is that processing and analyzing large datasets can be complex and time-consuming, making them less accessible for smaller businesses.
Benzidia, S.,et,al.	2021	Improved efficiency and reduced waste through data-driven decision making, leading to higher environmental performance and cost savings.	One limitation is the potential for the high cost and complexity of implementing and maintaining these technologies to hinder their adoption and effectiveness.
Shah, N. D.,et,al.	2018	Big data and predictive analytics allow companies to make data-driven decisions, leading to improved efficiency and increased profitability.	Big data and predictive analytics can only provide insights based on past data and may not accurately predict future events or changes.
Ferraris, A.,et,al.	2019	The ability to extract valuable insights and knowledge from large data sets to inform decision making and improve business processes.	Lack of efficiency in processing and analyzing large datasets due to limited resources and expertise.
Saggi, M. K.,et,al	2018	Increased efficiency and accuracy in decision-making, resulting in improved business strategies and higher ROI for organizations.	Lack of access to comprehensive and accurate data due to privacy concerns, limited resources, or data silos.
Gadde, H.,et,al.	2020	Improved data accuracy and efficiency through automated data processing and decision-making capabilities of AI.	The dependency on data quality and consistency can limit the accuracy and effectiveness of AI-enhanced data warehousing.
Shahid, N.,et,al.	2019	Improved accuracy and efficiency in decision-making which leads to better patient outcomes and cost-savings for the organization.	Limited ability to handle complex and dynamic data, resulting in potential errors or oversimplification of decision-making processes.
Secinaro, S.,et,al.	2019	The increased accuracy and efficiency in diagnosis and treatment, leading to improved patient outcomes and reduced healthcare costs.	Potential biases in algorithms can lead to inaccurate diagnoses or treatments, especially for marginalized or underrepresented populations.
Hariri, R. H.,et,al.	2021	Uncertainty in big data analytics can reveal new correlations and insights, leading to unexpected but beneficial findings.	Limited ability to accurately predict outcomes due to constantly evolving and complex data sets.
Endriyas, M.,et,al.	2019	“Understanding performance data allows for data-driven decision making and optimization of efforts leading to improved outcomes and productivity.”	The availability and accuracy of the data may be incomplete or unreliable, which can result in misleading conclusions.

DEVELOPMENT

With the following elements in mind, we formulated a model of interprofessional education to explore teamwork and communication in health care. Second, as the next step, it is better to start from a literature review to get insight into the extent of interprofessional education and its implications on teamwork and communication skills in healthcare. This will lay the foundation of the study and will provide a theoretical background on what can be potential research questions and objectives proper.Prepared statement will provide an insight into the concepts of the study. Secondly, the model suggests conducting interviews or surveys with practitioners from cross-disciplinary fields of healthcare to gain insight into their perspectives on interprofessional education and how it is being translated into actual collaboration and communication between disciplines in practice. Abstract: Interprofessional Education: Submit an abstract It will encompass first-hand experience, knowledge of what these people do on this topic, in clinical practice. Third, the model proposes a survey or observation of an education programs or interventions that appear to facilitate interprofessional education in a health setting. This will map out the optimal elements and approaches that promote collaboration and communication between team members HCPs. Figure 1 shows the Construction of proposed model.

Figure 1. Construction of proposed model

That new drug will go through multiple stages of research, testing, and approval before it can be introduced to the market; drug development is complicated. To streamline the process and make it more efficient, pharmaceutical companies use a data-driven approach called “optimizing drug development.” It starts with vast quantities of data being gathered and analyzed, including data from clinical trials, patient demographics, and historical data on drug development. Because of the extraction of huge amounts of data from previous clinical studies and databases, researchers can leverage such data analytics techniques to discover relevant patterns and trends that could inform their decisions regarding which drugs to pursue and how best to design appropriate clinical trials. Part of optimizing drug development is identifying out potential risks before they become issues. That means implementing data analytics to flag any red flags in the pre-clinical and clinical stages of drug development. By doing so you can help mitigate delays and roadblocks down the line, bringing the entire process to completion sooner. Healthcare professionals can save time and resources by automating certain processes and leveraging predictive analytics while ensuring their patients get the best possible care. Another critical application of data analytics in healthcare is positively influencing patient outcomes by identifying at-risk patients. This includes the analysis of a range of data, including eating habits, personal lifestyle habits, and so on. This way, we can ensure that treatments are tailored to the individual patients and increase the quality of care, while also giving insight into how to reduce possible future health issues for those patients.

RESULTS AND DISCUSSION

This approach for exploring the impact of data analytics on better decision-making in health care management consists of three {taking} steps — information assortment, information evaluation, and data-driven decision-making. Data collection (systematic collection, structured data, and selectivity of data), data quality (accuracy, completeness, and unmatched relationship), secondary data sources (administrative data, claims data), and use of electronic health records, administrative data, and patient surveys. This data is then classified and stored in a joint database for future use. Second, it is the advanced analytic techniques such as statistical analysis or machine learning to find the valuable insights in the data collected. It helps us identify patterns, make predictions, and find opportunities for improvements in healthcare delivery. And finally, when the insights are generated through data analysis, it can be leveraged to implement data-driven decision making to support the healthcare management. Such a process might preferably include planning, allocation of resources, and identification of possible quality improvement aspects.

Timeliness

Timeliness is an important performance parameter of data analytics in healthcare management.

Table 2. Comparison of Timeliness
No. of Inputs	Comparison Models
	BDA	AI	IPM	TA	Proposed Model
100	60,3	86,1	45,8	77,4	34,7
200	63,2	48,5	70,1	81,6	56,4
300	75,9	51,7	39,4	87,2	66,8
400	40,6	64,3	55,1	73,5	90,7
500	37,8	69,2	41,5	85,4	54,9

 It relates to how quickly data is collected, analyzed, and presented. Figure 2 shows the computation of Timeliness.

Figure 2. Computation of Timeliness

 Healthcare managers want real-time data to help resolve urgent situations and can spot emerging trends that they need to respond to quickly.

Accuracy

Much like reliability, accurate data is a critical technical performance parameter of data analytics.

Table 3. Comparison of Accuracy
No. of Inputs	Comparison Models
	BDA	AI	IPM	TA	Proposed Model
100	38,4	74,2	49,5	61,3	82,7
200	53,1	41,8	76,9	62,4	89,5
300	55,3	39,7	72,5	88,2	64,1
400	44,6	68,9	52,7	77,1	36,5
500	70,8	63,9	57,2	84,3	48,1

In healthcare management, flawed data could lead to dire results, including incorrect diagnoses or treatment plans. Figure 3 shows the computation of Accuracy.

Figure 3. Computation of Accuracy

Hence, the data analytics system needs to guarantee high data precision levels to enhance the decision process.

Scalability 

With the growing volume and complexity of healthcare data, data analytics systems should be capable of handling a large volume of data without degrading performance.

Table 4. Comparison of Scalability
No. of Inputs	Comparison Models
	BDA	AI	IPM	TA	Proposed Model
100	67,3	43,2	80,9	60,5	49,1
200	35,4	84,7	58,3	64,9	71,8
300	40,2	78,5	54,6	61,7	85,1
400	42,8	69,6	50,3	76,1	38,9
500	88,3	65,7	47,5	79,2	55,8

Figure 4. Computation of Scalability

The scalability option is one of the most essential features for healthcare managers to handle, process, and analyze data in their effective form so that timely and correct decisions are made.

CONCLUSIONS

Data up to October 2023 The healthcare sector is facing an ever-growing amount of data and because of this, data analytics has become an essential aspect in making decisions more effective. It is the art of finding an answer to the data by the use of technology and statistical methods and tools. Making decisions based on evidence: Healthcare managers are relying on data analytics, one of the major advantages of the technology. By aggregating vast quantities of data from electronic health records, patient demographics, and medical devices, etc., this provides managers with a well-rounded view of patient needs and organizational performance. In doing so, they are able to better make decisions which could lead to better patient outcomes and cost-effective operations. Another way in which data analytics can help healthcare managers is by discovering patterns and trends within data that can help them predict what will happen in the future. A manager can study the patient data and identify the patients who are at high risk and devise plans to keep the management cost low before the disease actually happens. Such caterings may enhance patient care, and will ease stress on the healthcare system.

· The upward trajectory of data analytics in healthcare management can be enhanced by introducing AI for driving more intelligent decisions. As a result, AI algorithms are able to process enormous volumes of intricate data, identify patterns and trends, and deliver more precise and efficient insights to aid in health care decision-making.

· Using data analysis, data analytics in healthcare management will become a future thing. In addition, the solution can be developed in a short time making the decision-making much better. So, it suggests answers to the problem being discussed

· One more future scope potential implementation is predictive analytics in healthcare decision-making processes. It provides information for a more informed decision and proactive decision-making, as predictive analytics predicts future trends and outcomes by examining historical patterns and data. AI allows the healthcare management to ensure improved resource utilization, risk management, patient outcome, etc.

BIBLIOGRAPHIC REFERENCES

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3. Shah, N. D., Steyerberg, E. W., & Kent, D. M. (2018). Big data and predictive analytics: recalibrating expectations. Jama, 320(1), 27-28.

4. Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), 1923-1936.

5. Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), 758-790.

6. Gadde, H. (2020). AI-Enhanced Data Warehousing: Optimizing ETL Processes for Real-Time Analytics. Revista de Inteligencia Artificial en Medicina, 11(1), 300-327.

7. Shahid, N., Rappon, T., & Berta, W. (2019). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PloS one, 14(2), e0212356.

8. Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC medical informatics and decision making, 21, 1-23.

9. Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big data, 6(1), 1-16.

10. Endriyas, M., Alano, A., Mekonnen, E., Ayele, S., Kelaye, T., Shiferaw, M.,& Hailu, S. (2019). Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia. BMC health services research, 19, 1-6.

FINANCING

None.

CONFLICT OF INTEREST

None.

AUTHORSHIP CONTRIBUTION

Conceptualization: Snehanshu Dey, Chetan Kumar Sharma.

Data curation: Snehanshu Dey, Chetan Kumar Sharma.

Formal analysis: Snehanshu Dey, Chetan Kumar Sharma.

Drafting - original draft: Snehanshu Dey, Chetan Kumar Sharma.

Writing - proofreading and editing: Snehanshu Dey, Chetan Kumar Sharma.