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Our Core Services

Healthcare Consulting

OSP's team of experienced healthcare experts offers quality healthcare consulting services for providers and health plans. Our team includes experts on enterprise health technology platforms, US government programs, digital innovation, and business operations.

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Enterprise Applications

OSP is proficient and experienced in building robust enterprise applications to address your complex and sophisticated software needs and execute your digital transformation lifecycle. We have comprehensive customized tools to solve your healthcare challenge.

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Healthcare Solutions Enhancement

Optimizing the healthcare system is essential for better productivity and quality performance. OSP's team of healthcare app developers can enhance and upgrade the IT healthcare solutions in modules, security, or interoperability sections with new custom components.

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Dedicated Quality Assurance

OSP’s QA team can resolve the challenges of disrupted workflow and application safety. We offer assurance in product quality with multiple levels of quality testing, including usability, performance, localization, and security testing.

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Explore Population Health Analytics

Population management in healthcare refers to the use of strategies, processes, and tools for managing and compiling health data for implementing population health analytics. It is essential for healthcare systems in the US because population management in healthcare focuses on value-based care. Effective management of population health and hospital management helps to organize large volumes of data and use it properly. However, sometimes it may not be possible to implement large-scale community health strategies due to lack of staff or other organizational issues.

OSP can develop custom population health analytics to ensure equity at all levels of the population health management strategy. Our customized health data and management solutions can stratify huge data, making it easy to organize and utilize. OSP’s custom population health data analytics enable simplify data conversion for health providers, which further stimulate better population health management.


Data aggregation can become an exhaustive and time-consuming process in the US healthcare sector. Aggregation of patient data using population health analytics allows healthcare professionals to improve the overall care delivery. Population health management models lead to optimized use of health data that promotes improved healthcare practices and payer process flow.

OSP can build health IT analytics that would simplify the data aggregation process and support an effective health management system. Our custom healthcare analysis adoption model would address security issues at each data source, thereby protecting private health data. We can help improve data quality and reliability through our customized population health management systems. OSP’s population health management strategy can also allow efficient monitoring of population health trends.


The US healthcare industry is vast, and it requires a system for assessing the past and existing population health trends and finding ways to improve healthcare services, such as integrated care. Population health analytics address these concerns on the micro and macro levels of the health system in the USA. Population health management programs provide solutions for improving patient care quality and transparency.

OSP an engineer population health management system for simplifying the management of clinical data, data diagnosis, and overall health management. We are a population health management company that combines new health analytics and population health solution tools. Our custom population health management analytics enable health managers to function better through real-time data and help them make informed decisions via CDSS. 


Effective and accurate data analysis is a crucial component of the US healthcare industry. It is extremely important for ensuring good care delivery by physicians, nurses, and other healthcare providers. Population health software fastens the processes of report generation and reviewing patient data and population health trends.

OSP can create population health analytics integrated with data visualization that would enable health professionals to observe and identify patterns in population health data. Healthcare providers can make correlations and perform data analysis efficiently using our custom HIPPA compliant population health software. OSP’s customized population health management programs can support data visualization in the form of infographics, interactive dashboards, and motion graphics. 


Population health management strategy in the USA requires health providers to maintain a balance between understanding population health trends and delivering patient-centric care. Population health analytics promotes a better understanding of health patterns and predicts the likely risks that may arise. Health interventions can then be planned through the healthcare analytics adoption model to mitigate the risks.  

OSP can develop population health analytics with data interoperability that would include risk scores and risk stratification techniques. Our custom population management strategies provide risk score and risk stratification details that divide patients based on clinical and lifestyle risks. A risk score would denote the likelihood of a single event, for example, hospital readmission. On the other hand, a risk stratification framework is a combination of various risk scores to create a holistic profile of patients under risk and their needs. OSP’s customized population health trends can allow healthcare providers to utilize risk scores for estimating costs and target interventions to prevent high-risk patients from aggravating their conditions. 


The US healthcare sector is constantly evolving, which raises the need for a system to match the electronic health records of patients to the correct individual. Although patient identification and matching form an essential part of the health system in the USA, it is a complicated process. Issues in patient matching can gravely affect value-based care and hamper the process of sharing patient data. This is where population health analytics serves useful in resolving such issues. 

OSP can build a cloud-based population health management program to offer efficient patient identification solutions. With our custom population health analytics, healthcare providers no longer need to have concerns regarding data entry errors, missing information, or typos. OSP’s population health software can improve data quality and eliminate issues of matching wrong data with patients or patient misidentification. We can help healthcare organizations deliver patient-centric care accurately.

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Population Health Analytics Development Services


Software Services to Facilitate Population Health Analytics 

  • Integration of healthcare analytics solutions for medical insights
  • EHR platforms with data visualization tools for a better perspective
  • Integration of EHRs with government databases for population health assessment
  • Tools for categorizing data as per age, gender, prior conditions, and others for insight
  • Integration of medical informatics to identify patients at greater risk

Development of Custom Solutions For Population Analytics

  • Visualization of patient data according to demographic and ethnic criteria
  • Tools for insights based on factors like ethnicity, location, and others
  • Options for classifying data according to symptoms and prior conditions
  • Interoperability with EHRs to aggregate population health data from multiple sources
  • The ability to share data and insights among coordinating agencies

Design of Software For Population Health Data Analytics

  • Tools for stratifying risk of patients
  • Automated reminders for tests and treatment administration
  • Alerts based on predictive analytics for faster response
  • Functions for large-scale patient engagement and outreach
  • Intuitive visualization of data for better insights

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Solutions We Offer

Latest Talks


HealthTech, Policy, and Challenges: HLTH Insights with Ben Leonard

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How to Use Artificial Intelligence to Become an Authority in Population Health Management

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Predictive Analytics in Healthcare: Everything You Need To Know

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Healthcare Analytics Solutions: An Integral Part of the Medical Industry

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5 Trending Types of Healthcare Analytics Solutions to Leverage the Power of Data

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The Map of Healthcare Analytics Domains

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Frequently Asked Questions

Population health analytics is the assessment of medical information of a large group of people to find insights about their health. This group could be composed of people from one ethnicity, a particular geographic location, gender, or other parameters. Assessing health based on these factors reveals important patterns which help public health experts make informed decisions.  

Analyzing the health of a population group helps doctors identify important patterns when it comes to certain diseases. These could be chronic diseases or infectious epidemics that spread quickly. By understanding the patterns, doctors and public health experts can know more about the nature of the disease and how it affects people. Subsequently, this enables them to take appropriate measures to curb the spread and improve public health

  • Allergy lists  
  • Vital signs  
  • Patient demographics  
  • List of symptoms  
  • Diagnoses  
  • Prescription data  
  • Results of tests and scans  

The main advantage of using population health analysis software is finding patterns in the disease. These could include how fast it spreads, who is most prone to it if a certain part of the demographic is more vulnerable, its symptoms, and so on. This insight allows doctors to know the nature of the disease, which can tell who is most vulnerable.

  • Data capture  
  • Data scrubbing  
  • Storage and management  
  • Data security  
  • Data visualization  
  • Regulatory compliance  

Population health analysis helps doctors understand how and why a certain disease spreads and affects people. This is possible by analyzing the medical information of many people suffering from it. The understanding derived from this enables doctors to predict the likelihood of a person contracting the disease and the severity of it. As a result, they can take appropriate measures to ensure good patient outcomes. After treatment, the insights derived from public health analysis also let doctors educate their patients about the disease.   

The first towards analyzing population health is to acquire medical data. This data is highly diverse and varied and also spread across disparate systems. It could include demographic information, test results, data on symptoms, and many other parameters. This information could be spread across EHR systems, health plans, clearing houses, wearable devices, and so forth. Being able to aggregate this data and store it makes up the initial step.  

After obtaining the data, scrubbing it for needless points and organizing it for assessment becomes important. Subsequently, appropriate healthcare data analytics software helps clinicians discover patterns in the data. This requires the use of specialized tools and software for the actual analysis. A team for data collection and management, alongside specialized tools, helps providers adopt population analytics into their workflows.  

Data analytics can assess a given volume of data to identify patterns in it. When it comes to healthcare, analytics applied to a large body of medical information of a population reveals actionable insights about their health conditions. This insight enables them to spot correlations between diseases and groups of people. As a result, doctors can know how a certain group of people belonging to a particular race, ethnicity, location, age group, or gender might be impacted by a certain disease.  

  • Analytics infrastructure  
  • Analytics technologies  
  • Healthcare information management system  
  • Data aggregation

  • Use of artificial intelligence and machine learning technologies for analyzing data  
  • Big data analytics  
  • Data aggregation from IoT-based wearables  
  • Cloud storage systems