Population health analytics is a way of using medical data, so that healthcare professionals can manage medical facilities better. Data analytics helps in devising a strategy for taking informed decisions about patient care and conditions.
Data is a useful tool to determine number of visits needed for a patient to recover completely. Keeping a track of hospital visits helps to manage emergency situations and averts overcrowding.
How Population Health Analytics Improves Annual Visits
Analytics enhances case offerings, gain revenues, and meet MIPS (Merit-based Incentive Payment System) requirements.
Population health analytics ameliorates quality care, treatment process, and overall health. It is also useful for improving annual visits of patients. The approach is to stratify patients as per the severity of their diseases or diagnostic procedures.
Grouping patients helps to determine the ones that will be requiring diagnosis at regular intervals and the ones that can go without checkups for longer durations.
Healthcare analytics which is backed with patients’ yearly assessments determines risk aspects and current conditions. Doctors give out prevention or treatment plans during annual visits that is based on healthcare analysis.
Medical data of population enables not only patients, but doctors too to take control of their health regimes. Population health analytics is useful during an epidemic or pandemic situation to improve annual visits.
For instance, as per a research during the Covid-19 pandemic, in order to curb transmission of virus to the healthcare staff or admitted patients, providers are deferring to preventive and elective visits such as annual face-to-face check-ups.
Electronic healthcare data on patient such as vitals, allergies, or prescriptions can complement annual visits. Real-time monitoring and the ability to intervene by the healthcare workers via telemedicine or telehealth applications poses an opportunity for patients to cut down their visits to once a year.
Drivers of Population Health Analytics
Population health analytics is primarily driven by two basic components – big data and healthcare management.
Big Data
As per a research by McKinsey and Company, big data in the healthcare industry can save $300 to $450 billion every year for America. The growth of big data is driven by investments in the EHR (Electronic Medical Records), workforce management, and practice management. For instance, Oracle, a software company based in U.S.A, is in talks about acquiring Cerner, an EHR giant for approximately $30 billion.
Big data prevents and predicts outbreaks of infectious diseases within a population, thus maintaining their health. During the Covid-19 pandemic, big data proved to be useful for tracking travel history of people under infection risk and analyze people leaving and entering the affected area. Researchers scrutinize data on infection symptoms and development of virus for a plausible healthcare solution.
Big data for population health analytics is acquired from various sources which are as follows –
1. EHR
Electronic health records includes data on demographics, lab results, vital signs, allergies, medications, diagnosis, and list of problems. A patient is exposed to multiple healthcare experts and nurses during medical procedures. This data is handled and exchanged with doctors and nurses for determining effective outcome.
Patients’ data provides insights on medical conditions for prescribing accurate medications and treatment plans, analyzing risks and concerns, and to ameliorate outcomes. Data from EHR is useful for patient stratification according to chronic, acute, and critical conditions. For this reason data from EHR possesses importance in the population health analytics.
2. Wearable Devices
According to a study, around 67 million people in America are using wearable devices to track their health daily. These devices keeps in check vital signs such as blood pressure, sleep cycle, oxygen, heart rate, and more. Healthcare professionals use the data from these devices to predict health conditions for present and future.
Medical staff utilize the data to monitor vulnerable patients or who are at risk of infection post discharge from hospitals. McKinsey and Company report suggests that 20 percent of the healthcare expenses are due to lack of adequate sleep, absence of exercise, and addictions. The approach to reduce these costs and to improve population health is to render data to the healthcare providers through wearable devices.
3. Public Health Records
Immunization, birth, and syndromic surveillance data is registered to share with the healthcare providers when needed. Public records detects chronic or high-risk patients, so they can be provided help when needed.
For example, cancer registry is designed for storage, data collection, and management of people diagnosed with cancer. Registry plays a crucial role during cancer surveillance as it points out efforts needed to reduce the increasing burden of cancer.
Healthcare organizations can expand collaboration and communication with the public health officials for enhancing public care. Public healthcare officials manage, monitor, and prevent infectious diseases with the data available through public records. For example, New York City health officials developed a program that utilizes EHR to send healthcare alerts to medical staff.
Healthcare Management
Healthcare is managed based on the medical data acquired and recorded. This data helps to improve overall management of a medical facility, and to improve monitoring of patients’ vitals. For instance, Vios monitoring system, is a wireless IoMT patient monitoring system. This is created to reduce medical care costs and improve patients’ outcome.
This device provides heart rate, pulse, and respiratory rate 24/7. Real-time data is available for doctors, therefore they can respond faster and mitigate critical situations whenever needed.
Financial, operational, and clinical data offers analytical insights to the physicians, to improve patient care. An efficient healthcare management will address quality care gaps within a population.
Quality care is the key aspect of healthcare management. Objective of care is to better patient management, decrease cost on care, and improve medications.
Population Health Analytics – A Case Study
The population health coordinators (PHC) of the Newton-Wellesley hospital, which is a member of Partners HealthCare system used analytics for chronic disease management, and to develop standard practices to improve patient outcomes and reduce medical costs of patients.
Population health coordinators supported the frontline medical providers to manage chronic conditions. PHCs offered support to improve patient care, preventive measures, chronic disease treatment, and cancer screenings. Along with EMR data, the PHCs leveraged analytics applications and Health Catalyst Data Operating System along with Partners Quality Insights.
The analytics and data are used to detect patients’ unmet requirements, collect summary of care data for treatment interventions, engage patients with their care, and grow the current communication between care teams and patients.
Analytical data allows PHCs to monitor preventive and chronic quality measures. They are able to timely manage and report data related to chronic disorders to the management, exception is the health maintenance reporting.
Data aids PHCs to determine standard practices and strategies for improving chronic conditions. When data indicates that a patients needs specific primary or preventive care, PHC alerts the patients and schedules follow-ups for medical care.
A data driven approach helped to improve cardiovascular and diabetes in patients. The hospital saw 4.6 percent increase in diabetic patients who were able to control hypertension. 8.5 percent growth in patients who were able to control cholesterol within guidelines. 6 percent rise in patients who achieved blood pressure control.
This study proves the usage for data to improve health analytics and care of people.
The Bottom Line
The best way to limit hospital visits and admissions is to build a strong and healthy community. Ways to achieve this is to maintain hygiene, exercise, and adequate diet. Also, technologies such as artificial intelligence in surgeries, IoMT, big data, and telemedicine, helps in enhancing patient care. Population health analytics can build healthcare management in the long run.