Benefits Of Health Data In Healthcare Organizations

The Importance of Health Data in Healthcare Organizations

Discuss about the Health System and Community Level Interventions.

Health data is of great importance in healthcare organizations as it helps the organizations to know the health status of their patients which makes it easy for them to offer treatments to the appropriate people when needed. Health organizations need to have the health data of not only the patients but also for the healthy people since they may need to offer some medications such as vaccinations to the healthy people and having the data of the healthy people prior to giving the medications will help them to know the best medications to give. With the health data, the healthcare organizations need to store the data well to avoid leakage to the unauthorized people since health data is very crucial and sensitive and should not be disclosed to the unauthorized by any means. The healthcare organizations analyze their data regularly to understand the data well and these help the organizations to understand the health status of the people especially their patients’ well and offer the most appropriate care and treatments to the patients according to their health demands. In this paper, we shall analyze the health data of UTS hospital to understand the health status of the patients well and make some meaningful inferences from the analysis.

Hospitals and the other healthcare organizations handle huge volumes of healthcare data. These healthcare organizations must always be very keen when handling and storing healthcare data since healthcare data is very crucial and sensitive and need to be handled well as required and by the right personnel to avoid loss, leakage to the unauthorized people, among many other problems which may arise if the data is not handled and stored properly (Yue et al., 2016, p.218) The healthcare organizations greatly need the healthcare data for their patients as they use the data to keep track of the health conditions of their patients and know the right treatments and medications to offer to help the patients to recover from the illnesses (Wager, Lee, and Glaser, 2017). The healthcare data of the healthy people is also required by the healthcare organizations as the healthcare organizations might need to offer some public medical services such as public vaccination or immunization and with the healthcare data of all the people (both the patients and the healthy people), the healthcare organizations will be in a better position to know which treatments and medications to offer to which people which will not overreact with their bodies (Hernandez, 2017, pp.186-194). We have some bodies which are very sensitive to some medications and these medications may cause allergy or other health problems to the people and therefore, the health practitioners must always be very careful when offering treatments to different people (O’Brien, 2016). Healthcare organizations do a detailed analysis of the healthcare data they collect from their patients and the other healthy people to have a deep understanding of the data and for them to know the best ways to use the data which will be of great benefits to the patients. The main aims/objectives of this paper are to discuss healthcare data in details and study its use in healthcare organizations and see some of the ways followed by the healthcare organizations to analyze the healthcare data for them to understand the data in a better approach. This discussion will help us to understand how effective and efficient the current methods of health data collection and data analysis in hospitals are in the modern field of healthcare. The paper will also discuss the main antenatal health problems and some interventions which can be implemented to address the problems. This whole discussion will be very helpful in answering our main research questions which are:

Methods of Data Collection and Analysis

Are the current methods of data collection, analysis, and usage employed by the modern healthcare organizations effective and have they helped to improve the overall efficiency or performance of the medical field?

Are antenatal or neonate health problems common problems in hospitals?

What types of interventions should be taken to address these health problems?

As already mentioned, healthcare data of the patients and the healthy people is of great importance in healthcare organizations. The healthcare organizations are ever collecting the healthcare data and storing the data as required for their own benefits and for the benefits of the patients and the healthy people whose data is collected. Having the health data of the patients and the healthy people helps the healthcare organizations to understand these people and thus offer the best and the required medical services to each of the people. There are many methods which have been invented to help in collecting and storing or recording of the healthcare data of the patients and all these methods help to improve the quality of the medical services offered by the healthcare organizations. One of the most common modern methods employed in the storage of healthcare data is the electronic health record (EHR) or electronic medical records (EMR) method. Electronic health record (EHR) is an electronic method which used in storage of the patients’ medical history which is maintained by the providers over some time. The health data of the patients stored using EHR may include demographics, the patients’ progress, the medical problems experienced by the patients over time, the medications offered to the patients, the vital signs shown by the patients, the radiology reports of the patients, among other data (Rumball-Smith, Shekelle, and Damberg, 2018, pp.26-31).

In Australia and some other overseas countries, we have some well-established AR-DRGs (Australian related diagnosis groups) and these AR-ARGs are normally associated with some diagnoses and some medications. The AR-DRG is an Australian based classification system which ensures there is a provision of a clinically meaningful way which relates the number and the type of patients treated in hospitals to the medical resources which are required by the hospitals (Jackson et al., 2015, pp.1433-1441). Each AR-ARG represents a particular class of patients who have similar clinical conditions and hence require similar medical services.

Over the last few years, the prevalence and the incidences of antenatal admissions to acute hospitals have been rising at an alarming rate. This rise results from an increase in the number of health problems and complications which affect the pregnant mothers. Some of the major health complications which have become very common to pregnant mothers include non-malignant breast conditions, vaginal complications especially during delivery, miscellaneous metabolic disorders, complications of the digestive systems, among many other health complications which have become very common in the modern world.

Antenatal Health Problems and Solutions

Different hospitals including UTS hospital have come up with some well-defined medical approaches and procedures to help these pregnant mothers who experience the antenatal health complications in order to improve their health and reduce the many cases of deaths resulting from antenatal complications (Mbuagbaw et al., 2015). Some of these approaches include establishing special sections (special medical rooms) for the pregnant mothers where they can get special attentions and the required medical services from the available medical experts, giving the first priority and the best medical services to the pregnant mothers who are always very prone to more serious health complications which can even lead to deaths if not attended to as fast as required, advising the pregnant mothers about their health status and some of the healthy practices such as the diets they should take and some activities which they should avoid for the sake of their health which is normally at great risk during pregnancies, among other relevant approaches (Downe et al., 2016, pp.529-539).

From the given raw data of the UTS hospital, we shall use pivot tables in Ms. Excel to do our analysis. Pivot tables are strong data analysis tools used in Ms. Excel which make it easy to extract the significance of large or bulky data, and this is very important in understanding the data as required (Visser et al., 2018). The methods which were used to collect the given data were the observation method and the interview methods (Hanvey, 2018, pp.19-46). The researchers closely observed the patients and the symptoms they portrayed and asked them some questions (interviewed) about their health conditions to understand them well. The hospital also provided some health data of the patients which it had saved for its medical use. Having obtained all the required data and using the pivot tables to get the significance of the data, we shall draw tables in Ms. Word and some bar graphs to help in interpreting and understanding the data well.

From the data, we can see that at the time of data collection, UTS hospital had 34,624 patients who had different health problems. From the pivot tables, we can see that we have 35,086 different DRG (diagnostic related groups) descriptions where we can see that among the major DRG descriptions with the patients are neonate DRG description with 2615 patients, vaginal delivery W/O complicating diagnosis with 1806 patients, colonoscopy with 650 patients, chest pain descriptions with 730 patients, other antenatal admission moderate or no complicating diagnosis with 632 patients, oesophagitis, gastroent & misc digestive system disorders descriptions with 560 patients, among others. A table showing the main DRG descriptions and the number of patients is shown below:

DRG description	Number of patients	Percentages compared to total
Neonate DRG description	2615	7.45%
Vaginal delivery W/O complicating diagnosis	1806	5.15%
Chest pain descriptions	730	2.08%
Colonoscopy	650	1.85%
Antenatal admission moderate or no complicating diagnosis	632	1.80%
Oesophagitis, gastroent & misc digestive system disorders descriptions	560	1.60%
Total	6,993

Data Analysis of UTS Hospital

From the data of the main DRG descriptions, we can draw a bar graph shown below to compare the DRG descriptions and the number of patients with the descriptions.

From the collected data and the pie chart shown above, we can see that the three main DRG descriptions which had the highest number of patients were neonate DRG description which had 2615 patients, vaginal delivery W/O complicating diagnosis with1806 patients, and chest pain description with 730 patients.

From the pivot tables, we can also deduce that among the most common AMO specialty cases are obstetrics which is most commonly used to address most of the female health complications especially those related to vaginal and uterine problems (reproductive system health problems), gynaecology also commonly used in addressing most of the health complications which affect the reproductive systems of females, general medicine used to address most of the general health problems experienced by both males and females, plastic surgery used to address the health complications experienced by both males and females, among other AMO specialties. From the data of the UTS hospitals, we can also see the number of females suffering from different health complications (the female patients) is by far higher than the number of the male patients which clearly shows that females are always at higher health risks as compared to their male counterparts (Torre et al., 2016, pp.182-2002).

From the data given by the pivot tables, we can also tell that the patients had different LOS (length of stay) in the hospital. We can see that among the patients who spent the longest time in hospitals were the patients with vaginal delivery W/O complicating diagnosis who spent 5008 hours in the hospital, the patients with neonate, AdmWt > 2499 g W/O significant O.R. procedure W/O problems who spent 7446 hours in the hospital, the patients with rehabilitation W catastrophic or severe CC who spent 6770 hours in the hospital, the patients with Schizophrenia disorders W mental health legal status who spent 4843 hours in the hospital, the patients with tracheostomy any age, any condition who spent 3582 hours in hospital, among others.

The data of the DRG description and LOS can be tabulated in the table below:

DRG description	LOS (hours)
The patients with vaginal delivery W/O complicating diagnosis	5008
The patients with neonate, AdmWt > 2499 g W/O significant O.R. procedure W/O problems	7446
The patients with rehabilitation W catastrophic or severe CC	6770
The patients with Schizophrenia disorders W mental health legal status	4843
The patients with tracheostomy any age, any condition	3582
Total	27,649

From the above data, a bar graph can be drawn as shown below to show the DRG descriptions and the LOS of the patients.

From the excel data obtained using filtering and the pivot tables, we can see that the six main diagnosis of the aboriginal people were the caesarean delivery W/O complicating diagnosis with 8 patients, vaginal Delivery W multiple complicating diagnosis, at least one severe with 11 patients, other antenatal admission W severe complicating diagnosis with 14 patients, neonate, AdmWt > 2499 g W/O significant O.R. procedure W/O problem with 16 patients, vaginal delivery W severe complicating diagnosis with 8 patients, and vaginal delivery W/O complicating diagnosis with 8 patients. This information can be represented by in the table and the bar graph shown below:

Conclusion

A table showing the main DRG descriptions of the aboriginal people:

DRG description	Number of aboriginal people (patients) with the description
Caesarean delivery W/O complicating diagnosis	8
Vaginal Delivery W multiple complicating diagnosis, at least one severe	11
Other antenatal admission W severe complicating diagnosis	14
Neonate, AdmWt > 2499 g W/O significant O.R. procedure W/O problem	16
Vaginal delivery W severe complicating diagnosis	8
Vaginal delivery W/O complicating diagnosis	8

From the data analysis section, we have seen that the top three most common DRG descriptions of the patients are neonate DRG description which has 2615 patients, vaginal delivery W/O complicating diagnosis with 1806 patients, and chest pain descriptions with 730 patients. This data clearly shows that these three health complications whose DRG descriptions are given are the most common and the most severe health complications which affect the people and some health measures should be put in place to address them. Some of the recommended health measures which should be implemented to address these health complications are:

To educate the pregnant mothers about their health status and some of the health status which they should observe for the sake of their health and their babies. Research has shown that most neonate health problems (health problems of the newborn babies) originate from the mothers due to their unhealthy practices such as taking drugs, taking unhealthy diets, among other unhealthy practices. To reduce these cases, the mothers should be keen to make sure they take the diets recommended by the health officers, avoid taking drugs when pregnant and avoid other unhealthy practices which can harm their health and the health of their babies (Kuzara et al., 2018, pp.1-14).

Vaginal delivery W/O complicating problems are other major health problems which affect the people. To address these problems, women should be educated and given enough knowledge about their reproductive systems and be advised on what measures and precautions they can take for the sake of their health especially the reproductive health which is very vital in their lives. Women should also be visiting doctors regularly to have a checkup of their reproductive health and get some medications when necessary and this will reduce those extreme cases of vaginal complications (Shah et al., 2016, pp.305-317).

Lastly, on the chest problems, the healthcare organizations should be committed to educate the people about their health status and concentrate more on the common chest/heart problems and advise the people some of the measures they can take to reduce the cases of chest or heart problems which are very deadly (Jordan et al., 2017, p.1194).

Conclusion

In conclusion, we can say that most of the healthcare organizations including our hospital (UTS hospital) have embraced the modern technology in their operations and this modern technology has helped to improve the efficiency of the healthcare organizations and the quality of medical services offered in a great way (Rozenblum et al., 2015, pp.3-22). The healthcare organizations have also taken some important health measures to address most of the common health problems which affect the people and this is of great importance in improving the health of the people.

References

Downe, S., Finlayson, K., Tunçalp, ?. and Gülmezoglu, A., 2016. What matters to women: a systematic scoping review to identify the processes and outcomes of antenatal care provision that are important to healthy pregnant women. BJOG: An International Journal of Obstetrics & Gynaecology, 123(4), pp.529-539.

Hanvey, C., 2018. Data Collection Methods. In Wage and Hour Law (pp. 19-46). Springer, Cham.

Hernandez, J., 2017. Medication management in the older adult: A narrative exploration. Journal of the American Association of Nurse Practitioners, 29(4), pp.186-194.

Jackson, T., Vera, D., Richard, M., and Steve, G., 2015. Australian diagnosis-related groups: Drivers of complexity adjustment. Health Policy, Volume 119, pp. 1433-1441.

Jordan, K.P., Timmis, A., Croft, P., van der Windt, D.A., Denaxas, S., González-Izquierdo, A., Hayward, R.A., Perel, P., and Hemingway, H., 2017. Prognosis of undiagnosed chest pain: linked electronic health record cohort study. BMJ, 357, p.1194.

Kuzara, J., Woodriff Sprinkel, A., Mekuria, F.T., Rubardt, M., Maguiraga, F., Sissoko, K. and Hastings, P., 2018. Addressing social and gender norms to improve uptake of maternal health services in Mali: a descriptive study of CARE’s Project Hope for Mothers and Newborns (PEMN). Culture, health & sexuality, pp.1-14.

Mbuagbaw, L., Medley, N., Darzi, A.J., Richardson, M., Habiba Garga, K. and Ongolo-Zogo, P., 2015. Health system and community level interventions for improving antenatal care coverage and health outcomes. Cochrane Database Syst. Rev, 12(12).

O’Brien, K.E., 2016. The Effects of Allergies and Anaphylaxis on the Body and Mind: A Survey of Opinions and Knowledge on these Disorders.

Rozenblum, R., Miller, P., Pearson, D., Marielli, A., Grando, M.A., Rozenblum, R. and Bates, D.W., 2015. Patient-centered healthcare, patient engagement, and health information technology: the perfect storm. Information technology for patient empowerment in healthcare, pp.3-22.

Rumball-Smith, J., Shekelle, P.G. and Damberg, C.L., 2018. Electronic Health Record. American Journal of Managed Care, 24(1), pp.26-31.

Shah, M.S., Letourneau, J.M., Niemasik, E.E., Bleil, M., McCulloch, C.E. and Rosen, M.P., 2016. The role of in-depth reproductive health counseling in addressing reproductive health concerns in female survivors of non-gynecologic cancers. Journal of psychosocial oncology, 34(4), pp.305-317.

Torre, L.A., Sauer, A.M.G., Chen, M.S., Kagawa?Singer, M., Jemal, A. and Siegel, R.L., 2016. Cancer statistics for Asian Americans, Native Hawaiians, and Pacific Islanders, 2016: Converging incidence in males and females. CA: a cancer journal for clinicians, 66(3), pp.182-202.

Visser, A., Seelye, M., Cassady, S., and Collard, D., 2018. Making Usage Stats Usable: Evaluating Usage Stat Tools and Maximizing Excel/Pivot Tables.

Wager, K.A., Lee, F.W. and Glaser, J.P., 2017. Healthcare information systems: a practical approach for healthcare management. John Wiley & Sons.

Yue, X., Wang, H., Jin, D., Li, M. and Jiang, W., 2016. Healthcare data gateways: found healthcare intelligence on blockchain with novel privacy risk control. Journal of medical systems, 40(10), p.218.