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Data Automation for Healthcare

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Data Automation for Healthcare: A walk through with Business Intelligence

Overall cost reduction and greater efficiency remain the top priorities for healthcare centers across the world. When the focus is on cutting costs and improving performance by slimming down waste processes, automation becomes a necessity more than a consideration. Over the past couple of decades, the healthcare industry has taken steps toward the digitization of operations and patient data handling. This shift toward digitized processes in itself has resulted in instant access to information, simplified information sharing, and consequently improved patient outcomes. As the data amassed in the process of digitization continues to mound, data automation for healthcare has gone from something that’s ‘nice to have’ to a ‘must have’.

A large cross-section of healthcare institutions today recognize the potential of automation in revolutionizing healthcare and making its day-to-day operations more cost-effective. The days of mammoth repositories of manually-maintained healthcare records stacked in thousands of files should be over even as data automation for healthcare, with an eye on leveraging business intelligence, enters the mainstream. While healthcare organizations are yet to tap into the full potential of opportunities offered by automation, it’s a start in the right direction.

What Is Data Automation in Healthcare?

Automation refers to the use of information technology to facilitate the completion of certain processes in a streamlined manner without the need for human intervention to bring about the desired outcomes. In the healthcare domain, automation technologies can be incorporated into a wide range of such processes, resulting in administrative workload reduction, elimination of wasteful practices, enhanced information exchange, improved and consistent patient care, meaningful data analysis, efficient patient monitoring. Besides cutting back on the monumental amount of paperwork that healthcare organizations have to deal with, automation can also help in increasing operational efficiency and reducing staffing costs.

A Journey With Business Intelligence

The healthcare industry has been in the thick on an influx of data ever since the Big Data phenomenon took form. With the amount of clinical data increasing manifold, business intelligence in healthcare has become the need of the hour. The primary reason for business intelligence assuming the forefront of data automation for healthcare is the era of the technological revolution we live in. As business intelligence tools become more affordable, healthcare organizations have an unprecedented opportunity to strengthen their operational process. Here’s how healthcare stands to benefit from this journey with business intelligence:

Improved Patient Care

Healthcare organizations already rely on digital tools and technologies to support their day-to-day operations with the end objective of improving patient care. Business intelligence coupled with data automation for healthcare can further this goal by bringing in elements of predictive analysis by using automation tools to ascertain parameters such as patient safety, patient wait time, patient satisfaction, illness and recurrence risk, potential treatment costs, possibility of readmission, average hospital stay. These parameters can, in turn, help healthcare professionals make informed decisions on patient care.

Health Records Analytics

Storing patient records electronically has pretty much become the norm. But what good is all that mound of patient data if it cannot be used to facilitate better patient care and management? This is where business intelligence tools come into play. They can pick out relevant bits of information from centrally stored patient data pools to facilitate better predictions and actionable insights.

Better Allocation of Resources

Another key advantage of combining data automation for healthcare with business intelligence is the scope for better management of resources by allocating them with need-based precision across departments, and thus, cutting back on waste. Since predictive analysis can help determine when a patient is ready for discharge, it also helps with better allocation of resources such as beds, medicines, and staff for patient care and helps reduce wasteful expenses.

Understanding Patient History and Lifestyle

Business intelligence tools have the capability to access downloadable data from health apps, as well as wearable devices, such as pedometers and fitness bands. This allows healthcare experts to leverage the ability of wireless technology to accurately track health metrics and information. This data can be immensely useful for healthcare practitioners in understanding patients’ lifestyles and history.

Affordability

Tech tools are becoming increasingly affordable, which is a driving force behind their growing appeal and adoption among end users. Business intelligence fits that pattern perfectly. It offers economical yet comprehensive solutions for enhancing the quality of services and operation in healthcare organizations.

Clinical Analysis

The scope of business intelligence extends beyond predictive analysis. It can prove to be an indispensable tool for prescriptive analysis given its ability to analyze clinical data such as lab results and test reports. These assist caregivers like nurses in chalking out more effective patient-care plans, focussing more on patients in need of extra attention and care.

Data Mining

Data mining abilities of business intelligence tools can help healthcare practitioners take stock of treatment plans in a more insightful manner by zeroing in precisely which aspects of a chose line of treatment are working and which are not. These tools can also be used to predict the exact outcomes of any given treatment procedure. This helps improve the quality of healthcare by helping organizations understand the deficits and take corrective steps.

The State of Health Analytics

Nearly all healthcare organizations today understand the importance of analytics in improving revenue cycles, performance, efficiency, and overall patient care. Yet, a majority of them do not have full-blown data analytics solutions in place. The current state of health analytics is best understood through the findings of a survey by Porter Research:

  • An overwhelming majority of healthcare organizations agree that business intelligence solutions and analytics can play a vital role in decision making.
  • Revenue cycle analytics and business intelligence rank high in priority for a majority of healthcare leadership.
  • Handling for denials and rejections emerged as a top challenge in the revenue cycle that can be corrected through analytics. Patient payments and billing processes come a close second.
  • The biggest hurdles in leveraging business intelligence and data analytics were lack of resources to tap into data analytics effectively, inability to benchmark analytics performance, and difficulty in channelizing analytics findings into actionable reports.
  • Organizations that have been able to overcome these challenges with the right strategy report have benefited from health analytics.
  • Some of the top benefits of health analytics include reduced A/R days and improved cash flow, increased revenue through the identification of bottlenecks in payment processes, and enhanced staff productivity.
  • Health analytics have proven to be particularly helpful in metrics such as claims submission, denials, rejections, cash flow, and billing.

Healthcare organizations are realizing the importance of robust data automation for healthcare for the sake of analytics and reporting programs now more than ever. Data analytics and subsequent reporting solutions will play a key role in implementing user-friendly processes that use actionable insights to facilitating improvement in revenue cycles and informed decision making.

Why is Healthcare Data Difficult?

Data analytics is a challenging proposition across industry verticals but even more so in the field of healthcare. While most other organizations struggle with issues of data storage, quality, access and integration, healthcare bodies also have to factor in more delicate aspects of security and privacy, data management, and retention.

Security and privacy are of utmost importance in healthcare. Any attacks on healthcare data can be extremely damaging for any organization as they not only come at the cost of financial losses but also reputation. Most of all, the biggest sufferer in case of any breach of data security is the individual whose private information, right from credit card details to lab results and diagnoses has been jeopardized.

Healthcare data is also difficult to manage, owing to its long-term retention, which means healthcare organizations need a far-sighted approach to determine how data gets stored, accessed and used. Besides, data management software for the field of medicine often has the scope of establishing periodic access privileges that give temporary viewing capabilities to different staff members from different departments on a need basis. These factors make it even more imperative for an organization to periodically review their data in order to delete, modify or anonymize the information.

The data entered into any health organization’s records also needs to be formatted, described, checked, for accuracy before being made accessible to different users within the organization for medical, administrative, and billing purposes. This velocity and volume further compound the difficulty of managing data in the healthcare domain.

The success or failure of data management strategies in healthcare is also dependent on accessibility. Mounds of data stored by an organization would amount to nothing if it’s not reported in the correct format and made accessible to the right people. In addition to all these universal challenges that make healthcare data a difficult proposition, some facility-specific difficulties can further complicate matters. For instance, a limited IT budget can be a big stumbling block in effective data management.

To combat these challenges, many medical centers now employ patient safety experts who not only possess medical expertise but also the ability to determine how data management practices can hinder or help patient safety. After all, identifying limitations is the first step toward coming up with effective solutions.

Progressing on Data Automation for Healthcare with Business Intelligence

The key takeaways from understanding the dynamics of data automation for healthcare with a focus on business intelligence are:

  • Healthcare bodies are fast transforming into repositories of data.
  • The desire for data automation prevails across the healthcare sector.
  • Healthcare units are seeking to employ business intelligence and analytics in four key areas – clinical, operational, administrative, and financial.
  • Handling healthcare data has its fair share of challenges, which inhibits the organizations’ ability to tap into the potential of data automation, business intelligence, and analytics to the fullest.

The answer to overcoming the existing challenges and hurdles in facilitating the progression of data automation for healthcare with business intelligence lies in a simple question: How to provide the right data to the right people at the right time?

The answer? Transforming reports to a single focussed delivery platform.

For this model to be successfully implemented, healthcare organizations but start with the very basics by:

  • Taking stock of their current operational processes.
  • Identifying the right business intelligence and analytics tools to work with.
  • Establishing the right deployment strategy with the top leaders in the organizational setup.
  • Engaging the IT department in the process and facilitating the training of their staff on the software and programs adopted for the implementation of business intelligence.
  • Focusing on peripheral aspects such as cybersecurity, user access, and data governance.
  • Making their staff comfortable with the use of any business intelligence tools by introducing concepts such as data visualization, facilitating skill upgrades, and practicing the techniques of data analytics to draw insights from the current pool of data.

A well laid-out, comprehensive plan toward data automation for healthcare can streamline the process of data scrubbing with minimal processing, resulting in a robust, upgraded future-ready system. Adoption of business intelligence and data automation for healthcare are only the beginning. Getting past this first crucial step opens up a world of endless possibilities.

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