Introduction
Over the last three decades, the Population Health Management system has been focusing on several aspects evidence-based, value-based, and patient-centered medicine. As a result, the continuous growth of Population Health Management requires effective processes and tools to collect data from multiple sources. This is where Data Analytics seems to make a great impact on Population Health Management and ensure effective population care. Hence, due to its growing utility, the Population Health domain has started to incorporate Data and Advanced Analytics in its sphere to improve community health at a wider scale with better precision.
What is Population Health Analytics?
The concept of Population Health Analytics derives from the basic idea of effectively managing Population Health. Here, Population Health Management refers to the process of collecting patient data from multiple health information systems and using them for the treatment and care of the patient. To give a greater boost to this fundamental healthcare understanding, Data Analytics is implemented and leads to optimized population outcomes or results.
Using Population Health Analytics the consumption of critical information becomes easy and the objective to deliver personalized health care remains strongly intact. Moreover, Data Analytics leverage the benefit of Artificial Intelligence in Population Health Management and focus on all “ what “,” who”, “how” of the entire Population. Hence, the three major aspects where Population Analytics can act smart are as follows.
- Population Health Outcomes
- Patterns of Population Health Determinants
- Population related Policies and Interventions
Use Cases of Population Health Analytics
The adoption of Data Analytics in the Population Health care environment has made it easier for the healthcare service providers to accelerate the community well-being practices and processes. Some of the key use cases of Population Health Analytics are.
| Use Cases | Description |
| Understanding the population flow | The intelligence-led Population Health Management allows the seamless alignment of service planning and delivery to enhance healthcare outcomes. The deep analysis drawn using Intelligent Machine Learning tools helps to understand the current patient flow and the routes they take. As a result policy formulation and development of protocols becomes refined with Population Health Analytics. |
| Enhancing Population Healthcare | Using the AI-driven Population Health Management strategies the providers are able to deliver value-based care rather than sticking to the “One for All” concept. Moreover, the use of effective wellness and prevention tools also provides cost benefits to the service providers as the wastage gets significantly reduced. |
| Addressing Social Determinants of Health | The most profound use of Population Health Analytics is identifying the social determinants of health. Based on such determinants the policies are so formed to combat health issues. These determinants can be social beliefs, environmental factors, lifestyle, etc. Furthermore, timely identification of the social vulnerability using Data Analytics allows assessing the actual and targeted gaps. |
| Improving Patient Outcomes | Timely serving the vast set of the population with diversified health ailments is near to impossible. Hence, Big Data makes it possible to address such a vast and distinct population by clearly explaining the patient metric and risk score. The key considerations to determine patient outcomes are age, gender, medical history, insurance, etc. Therefore, Big Data and Data Analytics provide a broader view of the patient’s state and requirement for resource allocation. |
| Developing Actionable Insights | By addressing the real-time problems using Big Data, the respective service providers can develop useful actionable insights. These are a result of deep and intensive analysis where priority patient groups are identified and needs are duly catered. |
Tools used for Population Health Analytics
There are several tools and techniques using which the organizations can make the best advantage of Population Health Analytics. Some of these analytics tools are:
| Tools | Description |
| System Analysis | It is the study of how the current system is performing in relation to the similar systems around. Further, System Analysis allows delivery of expected outcomes depending on the population –mix and other improvement initiatives. As a result, it provides effective care outcomes and delivery. The key tools used for System Analysis are Insights Population AnalyticsDeep –dive AnalyticsNational PHM Dashboards Digital Intelligence. |
| Opportunity Analysis | This analysis helps to determine the specific area of focus where data is used to identify opportunities. Based on this identification the efficiency, equity, and quality of the population care is determined and delivered. The core aspect of the Opportunity Analysis is detecting unwarranted fluctuations in and around the system. |
| Duplication Analysis | Also known as Deletion Analysis which designs and encompasses automated next-generation sequencing. Using the duplication analysis the reflex test for patients becomes fully automated and makes the clinical interpretation completely refined. Furthermore, it also builds Genetic Counseling and effective coordination between the at-risk members. |
| Quadruple Fail Analysis | It is the study of an event that occurs amongst the population due to high cost, low quality, and poor patient experience. As a result, creates an increased scope of inequalities and outputs. The information gathered using Quadruple Fail Analysis helps to frame the right interventions at the right time. |
| Risk Stratification Tools | It refers to the combination of tools that are a blend of objective and subjective data used to assess the risk level of the patients. Using these tools the patient’s risk level can be determined which further helps to put effective care management decisions in place and make the resources accessible to the patients in need. |
Population Health Analytics Companies
Population Health Management is the broader field comprising Care Management components. The technology companies are taking the lead in generating worklists for patients and allowing required interventions as and when required. These companies are categorized into three broader categories depending on their role contribution in Population Health Management.
| Companies | Description |
| Data Warehousing Companies | These companies provide databases for accessing large data sets and drawing comprehensive analyses. Hence, creates space for sophisticated and strong data management tools. These tools are completely generic Extract Transform and Load (ETL) tools. Some of the top-listed Data Warehousing Companies are. OracleIBMSAP |
| Healthcare Service Tools Companies | Such companies are new and emerging in nature and are known to provide Population Health Management tools. These companies are more preferred as they provide more specific software to cater to the Population’s Healthcare Management needs. Such models make facilitate care management work lists and include – EvolentCovisinti2i Systems Phytel |
| EMR Companies | These companies provide EMR software to manage Population Health in the organizations. The invasion of such companies in the Population Health ecosystem is backed by dilemmatic scope which is yet to be defined clearly. Some of the EMR companies include – Epic CernereClinicalWorks NextGen |
Metrics used for Population Health Analytics
The Population Health Management system is driven by certain metrics that are responsible for defining its efficiency. These metrics serve as the foundation on which Population Health strategies are framed. Hence, the widely considered KPIs for Population Health Analytics are as follows.
| Metrics | Description |
| Crude Mortality Rate | It is defined by dividing the number of deaths by the population exposed to risk in a particular period. Usually, this period is taken as one year and the rest depends on the overall size of the population. This helps to analyze the competing risks arising due to the specific disease. |
| Age-specific Mortality Rate | It is recorded as the death counts recorded per 1000 population in the same age category. Here it is assumed that the typical mortality pattern remains the same for the entire population in the given age group. Alternatively, it is also termed as Age-Specific Death Rate. |
| Life Expectancy | This is the statistical model to estimate the average time the human population is expected to live. It is calculated based on the year of birth, and demographical factors at large. The common assumption here is “Current Death rate remaining the same.” |
| Years of Potential Life Lost (YPLL) | It refers to the figure derived after subtracting the age of death from the pre-determined (standard) year and is further summed up across each cause of death. This helps to analyze how long the person would have lived there wasn’t any reason for premature death. |
Examples of Population Health Analytics
The use cases of Population Health Analytics can be explained broadly on various grounds. Hence, Data Analytics has helped in reshaping the Population Health Management ecosystem with great efficiency.
- Researching and Predicting Disease
- Automating Hospital Administration Processes
- Detecting Illness at earliest
- Reducing unnecessary Doctor’s visit
- Discovering new Drugs
- Analyzing accurate health insurance rates
- Streamlining and Sharing Patient Data
Final Words
The emergence of the Data Analytics and Intelligence concept in the Health Care sector has certainly given a great boost to the field and has completely changed the rule of the game. Being at its development phase (Population Health Management) is expected to undergo several transformations to enhance effective Care Management. Hence, the Data Analytics and Big Data tools and techniques are potent to allow a shift from individual care to population care with great accuracy and speed.
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