Unraveling the Process of Developing Healthcare Analytics Software

The healthcare industry has struggled in the past to manage population health, diagnosing chronic diseases at early stages, and predicting epidemics/pandemics. But, in today’s world where technology is booming across all sectors, it is easy to capture data and use it to our advantage.

Data is driving the growth of all industry sectors including healthcare. Data has seeped into every aspect of the healthcare industry and is helping to improve the quality of care and treatment through data analysis. Currently, the US healthcare analytics market is expected to surge at a CAGR of 18.9 percent during the 2024 to 2030 timeframe. The rise in technology trends such as big data, data analytics, and blockchain marks a monumental increase in the market size.

Healthcare data analytics is a powerful method used by healthcare providers and researchers for predictive analytics, clinical decision-making, and risk stratification. This method opens doors to managing population health and responding to pandemics like COVID-19 ahead of time. By developing healthcare analytics software for clinics and hospitals can mainstream the process of data analytics across the country.

Here’s what developers need to know while building healthcare analytics software.

Table of Contents:

  1. Types of healthcare analytics
  2. Benefits of healthcare analytics software
  3. Key features of healthcare analytics software
  4. Process of developing healthcare analytics software
  5. Cost of developing healthcare analytics software
  6. Challenges of developing healthcare analytics software
  7. Off-the-shelf vs. custom healthcare analytics software

Types of Healthcare Analytics

1. Descriptive Analytics

Descriptive analytics focuses on historical data to understand past events and trends. In healthcare, this technique analyzes patients’ demographics, medical history, population health, disease prevalence, and utilization of healthcare resources.

Descriptive analytics identifies patterns in these aspects and helps to develop care strategies to address rising concerns in health care. This technique also enables healthcare providers to make effective decisions for positive patient outcomes.

2. Predictive Analytics

Predictive analytics utilizes historical data and statistical modeling to forecast future outcomes in the healthcare sector. Using this technique, healthcare organizations can mitigate care risks, predict disease progression, and anticipate hospital requirements. By understanding all of these aspects, organizations can improve the quality of care and optimize care operations for better outcomes.

3. Diagnostic Analytics

Diagnostic analytics is a useful technique to determine patterns and trends in diseases. This method helps healthcare providers to uncover various treatment patterns – check whether treatment is working on the patient as expected or not. Based on the analytical reports, providers can change treatments and medications to more effective ones.

Furthermore, diagnostic analytics is also a great tool for clinical decision-making and spotting future possibilities of fatal or chronic diseases in patients.

4. Prescriptive Analytics

Prescriptive analytics processes data to decide on the optimal course of action. This type of analysis recommends the next steps and because of this feature prescriptive analytics is a crucial tool for data-driven decision-making.

Artificial intelligence (AI) and machine learning tools are used to skim through data effortlessly and quickly. This technique is considered to identify fraud in healthcare, automate interactions with patients, and gather data to improve healthcare apps.

5. Real-Time Analytics

Real-time analytics offers immediate insights into data for timely action. In healthcare, this technique holds significant value in generating real-time vital reports, detecting anomalies, and triggering alerts in real-time. Real-time analytics helps providers to make informed decisions at point-of-care and reduce adverse events.

Benefits of Healthcare Analytics Software

Notable benefits of healthcare analytics software include:

1. Improve Diagnosis

With healthcare data analytics software providers can analyze possible diseases a patient has contracted. This is done by scrutinizing medical data, tests, past medical records, and symptoms. Various technology integrations such as AI and machine learning (ML) help to analyze data faster.

A study conducted by GAO (U.S. Government Accountability Office) unearthed the benefits of using machine learning technologies to assist with the diagnostic process. GAO identified ML-based technologies for diabetic retinopathy, certain cancers, heart diseases, Alzheimer’s disease, and COVID-19.

2. Help Manage Inventory

Keeping track of medications, surgery tools, and other care resources is a consistent responsibility as it touches sound knowledge of logistics and finance. Having healthcare data analytics software allows healthcare organizations to forecast medication and other care requirements based on usage and admission patterns. The usage of predictive analytics techniques prevents unnecessary storage and expense of healthcare resources.

3. Cost-Effective in Nature

Developing healthcare data analytics software for a hospital helps to identify cost-saving opportunities such as items with incorrect pricing, unnecessary hospital readmissions, storing resources for a longer time than needed, etc. Data for all of this is available in real-time which helps hospitals to identify invoice errors and get accurate spend data analytics.

Further, healthcare data analytics software puts an end to data silos and issues such as legacy systems, system upgrades, reporting requirements, interface problems, etc. All of these issues pose major challenges in data analytics. Once these challenges are eliminated, the cost automatically reduces because money is no longer going to update the system, but there’s a single software that does all the analytical work for the hospitals.

4. Offer Multiple Integrations

Integrations are a must-have feature for any healthcare software because they prevent the need to redesign and develop software, or to complicate software with too many features.

Integrations work best with robust interoperability, so standards such as FHIR and USCDI play a crucial role in seamless data exchange between healthcare systems. Healthcare analytics software can be integrated with EHR/EMR, RCM, IoMT, healthcare CRM, RPM, and more such healthcare software.

5. Predict Patient Outcomes

The next benefit offered by healthcare analytics software is to predict patient outcomes. Machine learning algorithms integrated within the software check past medical data and offer insights on which treatment will work best for the patient.

By providing personalized treatments to patients, healthcare analytics software helps to improve patient outcomes. The software also predicts at-risk patients to change the course of patient outcomes into a positive one.

Key Features of Healthcare Analytics Software

Some features can be tailored while developing healthcare analytics software, but here are some of the must-have features for the software:

1. Regulatory Compliance: Implement regulatory standards such as HIPAA and HITRUST to maintain the privacy and security of ePHI (Protected Health Information). Failure to do so can lead to legal action.

2. Software Integrations: Integrations enhance the quality of healthcare analytics software and increase its scope of usage. Software integrations work well with robust interoperability. Some of the integrations to consider are EHR/EMR, remote patient monitoring apps, healthcare HR software, healthcare CRM software, healthcare asset tracking software, and revenue cycle management software.

3. Analytics Dashboard: Users spend most of their time on their dashboards, hence it should display insights on all factors such as finances, operations, patient engagement, inventory, etc. The dashboard can have a mixture of charts, customizable reports, and diagrams for healthcare data visualization and decision-making.

4. Data Management: Duplicating data in all systems can be a time-consuming process, hence integration of multiple software ensures that data is transferred seamlessly across healthcare systems. This simplifies the process of data management as users now have access to all data in one place.

5. Finance Analytics: This feature encompasses continuous monitoring of cash flow, overhead costs, ROI, outstanding payments, and treatment costs. It also sends out notifications for payments and dues, or insurance fraud. Finance analytics predicts future expenses and helps to curb unnecessary expenditures.

6. Data Storage and Processing: This feature allows real-time healthcare data processing and offers a healthcare data warehouse for reporting. Further, this feature captures unstructured data from all sources and stores the raw data, which is later converted into a structured format.

7. Patient-Generated Data Analytics: This feature gathers patient data for analytics which includes patient medical history, demographics, and clinical data. This feature analyzes the data for diagnosis, and treatment changes, and predicts care outcomes. Healthcare data is mostly collected from RPM apps, wearables, sensors, and other healthcare apps.

8. Patient Outcome Analytics: This feature analyzes data to understand trends and patterns between different treatment types, health outcomes, medications, and more. Based on analysis it predicts high-risk patients, patient volume, readmissions, etc.

9. Analytics for Hospital Operations: This feature calculates care KPIs like bed occupancy rate, ER waiting time, patient feedback, etc. This feature also analyzes key aspects of hospital operations such as equipment KPIs, pharmaceutical KPIs, supply chain management, patient load, nurse/physician-to-patient ratio, resources, operational bottlenecks, and more. Based on the analysis hospitals can streamline their operations.

10. Patient Engagement and Experience Analytics: This feature automatically scrutinizes factors like patient dropouts, patient loyalty, and patient portal engagement rates. It suggests ways to improve engagement based on analytical reports.

11. Clinical Decision Support Systems: This feature provides services such as alerts on health risks, assistance during diagnosis, clinical guidelines, medical image analysis, lab result analysis, and interdisciplinary support for complicated cases.

The Process of Developing Healthcare Analytics Software

Here are the key steps involved in developing healthcare analytics software for a care facility:

1. Partner with a Healthcare Software Development Company: A healthcare software development company is aware of rules and regulations associated with the healthcare sector, making them the experts in the field, hence partnering with them is a go-to option. Moreover, a healthcare software development company specializes in only healthcare products and is aware of what the audience wants and industry standards. Plus, the company offers customized solutions for their clients, which otherwise won’t be found if buying healthcare analytics software.

2. Define Requirements: Define requirements before the development process as it prepares developers for what to expect and use during the development cycle. Gather feedback and scrutinize the project scope to add requirements. Some of the essential requirements are compliance standards, integrations, analytics capabilities, cost estimation, resources, storage space, scalability, etc.

3. Research: Have multiple in-depth meetings with the business analyst, the project manager, and the software development team to explain every feature and functionality of the software. Based on this research plan the scope of the work, technology stack, and the approximate project cost. Don’t forget to check what the competition is doing and keep up with the technology trends.

4. Prototyping and Designing: Healthcare analytics software prototyping and design is an iterative process that involves creating prototype versions to visualize ideas and get feedback. Design is focused on user interfaces, data management, and functionality to ensure easy navigation and compliance with healthcare standards. Prototyping allows early detection of potential issues so that adjustments can be made before full software development.

5. Development: In the development stage, instructions are written and implemented in Python, Java, or C++ programming languages at both backend and frontend. Integrations are made with existing systems, and features such as data management, clinical decision support, and financial analytics are added to the software.

6. Testing, Release, and Maintenance: Next step is to test healthcare analytics software in real-world environments, and see if any changes need to be made. Even after releasing the complete product, there’s still work left. This may include reworking workflows, reworking design elements, reducing features, or adding new features.

Cost of Developing Healthcare Analytics Software

The cost of developing healthcare analytics software can range anywhere between $100,000 to $1,300,000. The cost is subject to change because it depends on aspects such as:

  1. Software licenses
  2. Opting for off-the-shelf or custom healthcare analytics software
  3. Have legacy systems or newer ones
  4. Legal fees
  5. Human resource
  6. Research and development costs
  7. Training expenses
  8. Marketing and advertising expenses
  9. Data storage and backup costs

Take into consideration all of the above aspects before estimating a budget for developing healthcare analytics software for your organization.

Challenges of Developing Healthcare Analytics Software

If leveraged efficiently then healthcare analytics software can be a boon for healthcare organizations otherwise it can hamper operations and care quality significantly. Here are a few notable challenges of developing healthcare analytics software that every developer and care facility must know:

  1. Interoperability: Without seamless data exchange, data analytics software won’t work efficiently. It requires data from sources such as EHR, wearables, RPM, healthcare CRM, etc. to maintain operations throughout the organization and deliver timely care.
  2. Burnout: Healthcare providers lead a busy schedule and tend to face burnout due to excessive documentation work. In such times, burdening them with additional unprocessed EHR data can add to the stress, resulting in them leaving the practice. With efficient analytical software, such scenarios can be avoided.
  3. Not Having Proper Data Standards: Abbreviations, data formats, unstructured data, units of measure, etc., can create inconsistencies in healthcare data. The software must adhere to standards such as FHIR and USCDI frameworks to maintain interoperability and avoid data inconsistencies.
  4. Security: Healthcare data consists of vital patient information, hence it must be guarded against cyber attacks. Healthcare data leakage is liable to legal actions and the concerned party faces charges. So, incorporate regulatory compliance such as HIPAA to maintain security.
  5. Data Volume: Huge data volume may cause the software to slow down and hinder the analysis process. So, consider implementing scalable and flexible software to avoid analytical blunders such as diagnostic errors.

These are some of the challenges to keep in mind while developing healthcare analytics software for hospitals and clinics. Plan out the development process ahead of time to avoid last-minute pitfalls.

Off-the-Shelf vs. Custom Healthcare Analytics Software

Off-the-shelf healthcare analytics software is a pre-built solution that is available on a subscription basis or purchase. They have built-in features and functionalities that may or may not be altered as per the requirements of a healthcare organization. Off-the-shelf analytics solutions are easy and quick to implement, and they lower upfront costs, but cannot be personalized.

Whereas, custom healthcare analytics software is tailored to meet the specific requirements of healthcare organizations. Though it requires high development time and costs, it is more flexible and scalable and ensures high ROI in the future.

Developing custom healthcare analytics software is a specialty of Arkenea, a healthcare software development company. We offer multiple healthcare software solutions such as practice management, hospital management, integrations, and more if you’re looking to develop personalized healthcare software for your organization then connect with Arkenea.