Leveraging big data in healthcare: How custom software can drive insights

Introduction

Big data is a high-volume, high-velocity, high-variety, and high-veracity data set created by health care providers, payers, and consumers. This big data generates value because it allows healthcare providers and insurers to predict patient outcomes better, identify trends, and make decisions with better and more appropriate data that can enable new ways of treating patients or running a healthcare operation. Through the analysis of large data sets, physicians can find ways to treat diseases more effectively, and this could aid insurers in identifying which patients are at higher risk of developing certain illnesses, assuming that this information is properly collected, stored, and analyzed. This would allow physicians to treat patients in a more personalized way. In turn, insurers could raise rates for high-risk patients (and even reject them) and offer lower premiums to low-risk patients. Moreover, electronic health records render real-time information, which can improve efficiency in business planning with providers and insurers and provide better cost-structure information.
Creating custom software solutions is key to realizing the potential of big data in healthcare, as they can manage data specifics and complexity. Custom-built solutions can offer advanced analytics features, real-time reporting, and secure integration with existing systems. Additionally, custom software improves the ability to manage and process big data, allowing for more actionable insights for smarter decision-making and operational practices. By building custom software, using big data analytics will become more meaningful and beneficial for patients and healthcare providers.

Understanding big data in healthcare

The four Vs (or four I’s: information, interest, interaction, and incentive) characterizing big data in healthcare include volume, velocity, variety, and veracity. Volume relates to the unparalleled availability of daily data from health systems, hospitals, life sciences and biomedical laboratories, household products, wearable devices, and the web and social media. The velocity of the process of data generation and analysis is of critical importance, especially when knowledge is needed about the onset of an outbreak. Analyzing genetic and pharmaceutical data with accuracy and timeliness, for instance, could be vital for preventing and treating cancers and diseases. The variety of data originates from multiple sources, such as electronic health records (EHRs), digital health data, self-tracking devices, patient surveys, and digital images. The issue of veracity concerns the credibility of the information processed by the digital systems and the extent to which related decisions may impact the population's health.
Historically, clinical data from electronic health records (EHRs) and health records, medical device and wearable data such as vital signs and patient metrics data, claims data captured by health insurers, genomics data captured in the lab, and patient-generated health data gathered through patient surveys and mobile-health (‘m-health’) apps have all been inaccessible to each other. However, through data-sharing and integration of data sources, scientists and providers can now see the big picture and empower each other to deliver better care and improve patient health outcomes.

Role of custom software in big data analytics

Tailored data management solutions

Custom software can be a powerful tool for managing large amounts of data in the healthcare sector, providing specialized solutions for the very specific needs of working in healthcare environments. Since, typically, generic solutions are not designed to work with a large amount of heterogeneous data from multiple sources, including electronic health records (EHR), wearable devices, and surveys of patients, amongst many other examples, the management and organization of different data types become massive challenges for generic solutions. On the other hand, with custom software, data can be a way that suits the workflows of the healthcare worker who will be using the system, which helps to reduce the risk of counteracting data silos as well as data inconsistency, ultimately improving the accuracy of data and its accessibility.
Furthermore, a custom software solution that is able to integrate disparate data sources can assist care teams with patient outcomes because they can combine and analyze information from different systems into a single window for all users. Integrating patient data means that decision-makers have all the information they need at their fingertips and, therefore, can support better collaboration around care. Improved access to complete patient histories and current health metrics in one place is also facilitated through custom-developed solutions. This, in turn, supports a smoother patient journey as all data can be seamlessly tracked and stored while relevant information for care decisions is communicated to hospitals and other health facilities in real-time.

Advanced analytics capabilities

A custom software approach facilitates using big data for advanced analytics, incorporating machine learning and predictive analytics to convert raw data into predictive medical insights and decision-making. Algorithms with specific analytic requirements can be designed to meet various healthcare organizations' desires and data sources. For example, patterns and trends could be identified to predict patient outcomes or disease transmission, triggering early intervention for personalized disease management and treatment plans.
However, other predictive analytics features – such as its ability to leverage advanced data processing techniques that can deal with complex healthcare data – would also be impossible without custom software. Two higher-level aspects of custom software that help it support advanced analytics in real-time care include the ability to run batch processes on huge amounts of data in real-time and the ability to return detailed and insightful data-powered answers in response to varied queries. A custom solution featuring cutting-edge analytics can learn a patient’s care needs promptly. Utilizing these advanced analytics features, custom software can fine-tune real-time care to best suit an individual patient’s care needs. This kind of data-powered comprehensiveness allows healthcare organizations to assess care practices, make inferences, pinpoint opportunities for improvement, and ultimately enhance their entire operations. This is why healthcare analytics in itself isn’t enough to keep up with modern healthcare needs – custom software is the key. Ultimately, this close connection between real-time care and advanced analytics proves that custom software development truly has a powerfully beneficial effect on each other.

Real-time data access and reporting

Access to real-time data and reporting is vital for healthcare management, and access to such things is exactly where a large advantage in custom software can be had. Custom solutions can provide healthcare providers with superior capabilities for real-time data visualization as it is being collected, rather than waiting 12 hours for each day’s batch of data to become available. Such dashboards can show a highly curated dashboard of relevant data in an easy-to-read format, making it possible to respond to changes in patient status and conditions sooner because such data can be seen as it comes in. For a highly dynamic healthcare system with many ever-changing patients states and conditions, access to such data in real-time can have a palpable impact on effective service delivery and the financial state of the operation.
Custom reporting tools can augment real-time data access by providing tools to create custom reports that fit organizational needs and reporting requirements. These tools can be built to cater to the requisition of the reports and can be modified at your convenience. These reports can be generated on demand and can be handed out to the respective reporting agencies containing the latest available information to assist them in catering toward better information and effortlessly achieving the goals of the organization. This customization allows the reporting processes to become streamlined. It reduces the intervening time typically associated with report preparation while also helping ensure that the organization is well-informed with all the information it needs.

Applications of Big Data and Custom Software in Healthcare

Improving patient outcomes

It has changed the way patients are treated by using predictive analytics to pinpoint health issues before they reach a critical stage. For instance, machine learning algorithms can analyze data related to patients to inform them when a chronic condition is appearing or could appear; these models can even detect disease outbreaks and respond appropriately. Providing better treatment by tailoring it to the characteristics of the patients, along with incorporating predictive models used by big-data analysis into custom software, has led to significant improvements in better managing diabetes, cardiovascular diseases, and other conditions.

Operational efficiency

Custom software and big data analytics help reduce operational costs associated with running a medical facility by shortening administrative work and optimizing resource allocation, leading to better operational efficiency. Sophisticated custom solutions automate clerical work such as scheduling patient appointments and sending billing notifications, which would otherwise require paid staff time. Furthermore, data analysis can map how resources are used, which helps preempt managerial decisions in assigning staff to duties and how equipment is utilized. Data might reveal demand-supply patterns that help early signal, for example, a particular surgeon’s busy days so that other staff can be shifted to provide care and minimize delays. Big data analysis also helps identify trends and push boundaries to cut overhead costs without compromising the quality of care. By saving time, optimizing workload, and helping staff do their job faster, leftover time can be spent on training and professional development. Implementing custom solutions and leveraging data allows the health industry to improve services.

Research and development

The use of big data and custom software in the field of medicine helps with experimentation and research teams by allowing researchers to analyze more data faster to gain new insights into diseases as well as drug creation. Big data can be used to discover new biomarkers and allow the creation of new therapies and more personalized medicine. Due to the human biases found in medicine, computational methods, as we now know, can find hidden advantageous correlations that may not be immediately obvious. For example, personalized or precision medicine advancements would not have been possible if we could not analyze more data faster and more accurately. The most apparent field where big data was used to improve medicine is the field of oncology or cancer care. Approaches to cancer care have been revolutionized by our ability to analyze large-scale data examples in search of dominant outcomes, helping point us in the direction of advances in how we treat patients. Furthermore, thanks to custom-made software specifically useful for research purposes, data management and analysis features grow faster than ever, leading to faster progress in medical science day in and day out.

Challenges and considerations

Data privacy and security

Issues with data privacy and security are a major concern when it comes to the implementation of big data solutions and custom software in modern healthcare. Given the strict regulations on matters related to patient information and data security, including HIPAA in the United States, GDPR in Europe, and other similar laws and regulations in various countries, it’s imperative to proceed with due attention to legal implications. Thus, each data point should be processed with the utmost care to avoid potential legal issues when it comes to custom software development. To ensure end-to-end data protection, advanced data security measures such as encryption, access controls, data security monitoring, and periodic security audits must be developed. Being on top of data security measures helps patients comply with the relevant regulations and makes them feel at ease, knowing that their data is secure.

Data integration and interoperability

As the number of offers grows, data collection from various sources will pose an enormous challenge to ensuring interoperability when launching big data in healthcare. Usually, each institution employs a number of different systems and technologies, especially in the age of digital health innovations that incorporate various technologies in healthcare. Such systems already pose a number of problems with data integration and compatibility, and they would certainly complicate the utilization of big data in healthcare. Custom software must be able to connect with existing information technology infrastructure, collecting streams of information from all these systems and devices, ranging from EHRs to wearables and patient surveys. In this manner, we would have something that could provide an interoperable system – that is, the one in which data can move freely between different systems. Unfortunately, however, this does not happen as easily and naturally as one might expect.

Cost and resource allocation

Negotiating these tradeoffs between investment in custom software solutions and big data advancements, on the one hand, and the expected benefits, on the other, is critical. Properly designing and integrating these technologies can require significant investments and commitments of time and money. Whether or not it is worth the investment is reflected in the ROI analysis, which will consider not only the immediate costs involved but also the increased operational efficiency, better patient outcomes, and research opportunities that come later. Healthcare organizations must plan carefully and do a cost-benefit analysis to invest in custom software and big data analytics that improve operational efficiencies and patient outcomes and justify the return on investment.

Conclusion

Big data is a huge opportunity in healthcare, and custom software is how organizations can take actionable advantage of the data to both transform patient care and extricate themselves from the traditional system of incremental innovation. Healthcare has a responsibility to continue to leverage this kind of software to make better use of its vast and messy data streams, to deliver more effective treatments at lower cost, and to conduct the kind of high-quality research that will lead our global society to a healthier future. However, for this to happen, providers must continue to confront data privacy challenges with the utmost sensibility in protecting patients’ rights and maintaining personal privacy. Providers must also face the challenge of data integration and cost. There is no end to the opportunities that one might assign to custom software to unlock the potential of big data in healthcare as it evolves.