Construct a dashboard that lists the health needs based on the community needs assessment that was performed and the critical data sources and data sets needed for the population health management program your health system is planning to launch.

Hossin Mosaddad

Rasmussen University

Population Health

Dr. Merle Point-Johnson

Deliverable 3 Submitted August 8, 2021

Diabetes has become a common disease among many adults, both men and women, in Seminole County, Florida, which has significantly led to most individuals experiencing adverse effects on their health and well-being. Diabetes is a health condition that occurs when the blood glucose of an individual, commonly known as blood sugar, is too high. Blood glucose is usually the primary source of energy in human beings, and it is obtained from the food that we eat. A hormone that is made by the pancreas, insulin, assists glucose acquired from food to get into the cells of human beings in order to be used for energy. The main types of diabetes that the population in Seminole county is suffering from are type 1 and type 2 diabetes. Type 1 diabetes is described as an autoimmune illness and the immune system attacks and damages cells in the pancreas in which insulin is made. Both men and women who have type 1 diabetes, their body does not produce insulin, and in order to stay alive, they must take insulin each day.

Type 2 diabetes normally emerges when an individual's body becomes resistant to insulin which leads to sugar building up in the blood. Individuals who have type 2 diabetes, their bodies do not generate or utilize insulin properly, and this is the most common type of diabetes among many people in Seminole county. The common symptoms of diabetes include sores that do not heal, extreme fatigue, blurry vision, frequent urination, loss of weight, increased thirst and increased hunger. Besides the overall symptoms of diabetes, men with this disease might have a diminished sex drive, erectile dysfunction and poor muscle strength, while ladies may also experience symptoms like dry and itchy skin, yeast infections and urinary tract infections.

Big data is very important to every undertaking of a health care organization, and it is defined as the abundant health information which is obtained from several sources. Big data is distinguished from electronic medical and human health data used for decision making by certain features, including being obtained from numerous sources, it is extremely variable in structure and nature, it moves at a high velocity and spans the enormous digital universe, and it is available in extraordinarily high volume. Some of the major sources of big data include medical devices, wearable devices, pharmaceutical research, payer records, genomic sequencing, medical imaging and electronic health records.

An electronic health record is a digital form of the paper chart of a patient. Electronic health records are usually are centered on the records of patients; they are real-time, can be acquired instantly and safely by authorized users. These records are computerized and contain certain information such as the patient's billing information, hospital discharge instructions, lab test results, immunization status, allergies, medicines, health history, ethnicity, gender and age (Cowie et al., 2017). Health care providers can share these digital health records if they are in the same health care facility, clinic or health care system. For instance, in case a doctor orders a laboratory test, the other health care providers will also be able to see the results. Whenever a clinician puts a patient on a new medicine, the other clinicians will also be able to see the type of medicine that was prescribed to the patient; hence there will be less chance of a clinician prescribing medicine that could lead to problems in case it is used with another medicine.

The use of electronic health records will significantly help in enhancing how well health care providers talk to each other and coordinate the treatment of patients well and improve the care of patients. Some of the benefits of electronic health records include security, education and fewer mistakes. There are always chances that papers records can get lost or be misfiled; hence electronic health plays a key role because there is less chance of those things happening, and the majority of the records are protected by passwords. Patients are also able to see their medical files, which lets them participate in their own health care. Patients are able to see their test results, keep track of things like glucose, check errors and review medical instructions given.

Wearable devices are also a source of big data in health care which has been advanced by wearable technology. Wearable technology in health care comprises various electronic devices that customers can wear and are meant to gather the data of users’ individual health and exercise. The improvement of wearable technology has enabled many patients to take control of their own health, which has greatly impacted the medical industry, comprising technology companies, providers and insurers, to create more wearable devices. Some of the common wearable devices that can be used include wearable biosensors, blood pressure monitors, ECG monitors, smart health watches and wearable fitness trackers.

Wearable biosensors hold the possibility to revolutionize remote healthcare ad telemedicine because these devices are portable and can be acquired in different forms like implants, bandages, clothing and gloves. These devices develop two-way feedback between the user and their health care providers, which enhances continuous and noninvasive diagnosis of disease and monitoring of health from physical motion and fluids of the body. Wearable blood pressure monitors usually measure blood pressure and the daily activities of the users, including the steps that they take to burn calories.

The various sources and types of big data have several advantages, such as generating real-time alerting, enabling improved health care with fitness devices, delivering greater insights into the cohorts of patients and ensuring there is the reduction of general health care costs. Other benefits of big data include easing the diagnostics of patients with electronic health records, predicting patients at higher risk quickly and efficiently and improving the health care of patients (Dash et al., 2019). Clinical insights are obtained from the knowledge that is derived from big data analytics, which improves patient care in the health care system because health care providers are able to prescribe effective treatment and make clinical decisions that are more accurate.

Data elements typically describe the logical unit of data, and these elements can greatly help the health care providers to make immediate gains in the well-being of patients as they develop the best practices for future initiatives. Some of the specific data elements that can be used by health care providers include the name of the patient, their age, ethnicity, gender, city, country and state (Bruland et al., 2016). Other data elements that can be required include billing information, hospital discharge instructions, laboratory test results, immunization status, allergies, medicines and health history. Every data element is usually defined by the type of information it represents. For instance, when distinguishing numeric data from text data, the data element will describe code values that should be observed with a certain type of data, minimum and maximum length and the data type of numeric, alphanumeric, time or date. Some of the common features that are included in the descriptions of data elements comprise the ID of the element, the name, the type of data, input and output formats and validation criteria to ensure that correct information is captured by the system.

Learning to use more readily accessible information such as ADT alerts, ICD- 10 codes and demographics is very important because it will promote incorporating more complex and different big data into the population health management ecosystem. Demographics serve as crucial information, and in order for health care providers to make sure that there is equitable treatment of outcomes, they should look at a wide range of the patients' personal information. This personal information can include the ethnicity, state, race, gender or age of the patient. Demographic information can significantly assist in informing treatment plans since physicians normally create standardized processes for collecting data and ensure that all patients have the potential to reach optimal treatment outcomes (Dinov, 2016). The ethnicity and race data can be used to improve the quality of care for all patients since it helps in developing more patient-centered devices, evaluating whether the practice is delivering culturally competent care, distinguishing which populations do not achieve optimum interventions and identifying and dealing with differences in care for certain populations.

Learning to use ICD-10 codes is also important because these codes have various advantages in the health care system. Some of the benefits of using ICD-10 codes consist of tracking public concerns and evaluating risks of adverse public health events, inhibiting and detecting health care fraud and abuse, refining clinical, financial and administrative performance and monitoring utilization of resources (Khokhar, et al., 2016). Other advantages of using ICD-10 codes include setting health policy, enhances carrying out of research, designing payment systems, processing claims of reimbursement and measuring the quality, safety and efficacy of care.

In conclusion, every individual should strive to ensure that they prevent and control diabetes disease by taking appropriate measures like avoiding foods with a lot of calories and taking part in exercises regularly. Every health care organization should also ensure that they implement appropriate big data sources like electronic health records, which offer various advantages like the security of information and eliminate errors that might be experienced in paper records. Health care providers should also learn to use more readily available data in order to promote the safety and efficacy of care to all patients.

References

Bruland, P., McGilchrist, M., Zapletal, E., Acosta, D., Proeve, J., Askin, S. & Dugas, M. (2016). Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. BMC medical research methodology, 16(1), 1-10. https://doi.org/10.1186/s12874-016-0259-3

Cowie, M. R., Blomster, J. I., Curtis, L. H., Duclaux, S., Ford, I., Fritz, F., & Zalewski, A. (2017). Electronic health records to facilitate clinical research. Clinical Research in Cardiology, 106(1), 1-9. https://doi.org/10.1007/s00392-016-1025-6

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1-25. https://doi.org/10.1186/s40537-019-0217-0

Khokhar, B., Jette, N., Metcalfe, A., Cunningham, C. T., Quan, H., Kaplan, G. G., ... & Rabi, D. (2016). A systematic review of validated case definitions for diabetes in ICD-9-coded and ICD-10-coded data in adult populations. BMJ Open, 6(8), e009952. http://dx.doi.org/10.1136/bmjopen-2015-009952