Critical Analysis of the Use of Secondary Health Data for Influencing Healthcare at Population Level

Verified

Added on  2023/06/18

|37
|16032
|117
AI Summary
This project critically analyses the ways in which secondary health data can be used to influence healthcare at the population level. It evaluates the importance of secondary data in healthcare sector, areas that can get affected or influenced by this data such as cost effectiveness, epidemiological applications etc.

Contribute Materials

Your contribution can guide someone’s learning journey. Share your documents today.
Document Page
Critically Analyse

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
Contents
PROJECT 1.....................................................................................................................................1
REFERENCES..............................................................................................................................11
Project 2.........................................................................................................................................17
REFERENCES..............................................................................................................................28
Document Page
PROJECT 1
Abstract: this project had focused upon critical analysis of ways in which secondary health data
can be used for influencing health care at a population level. This project had critically evaluated
importance of secondary data in healthcare sector. Areas that can get affected or influenced by
this data such as cost effectiveness, epidemiological applications etc.
For bringing improvement in the services, there is a constant need to identify the areas
where modifications need to be made (1). Secondary health data plays a vital role in providing
valuable information to the health organizations thus enabling them to work for the betterment of
the services (2). Various sources such as consensus, organizational records and information
gathered by departments of government are important sources of secondary health data (3). The
purpose of the present essay is to critically analyse the ways in which secondary health data can
be used to influence healthcare at the population level. Information about various factors such as
cost effectiveness of data extraction, its epidemiological application and accessibility of data
have been discussed.
Healthcare at population level and Secondary health data
The concept involving healthcare at population level comprises shifting the focus from
individual level to the whole population (4). In this, a broader range of factors are addressed
which impact the health of the whole population (4). Improving healthcare at the population
level not only includes the health information but also other factors that impact it (5). This may
include the surrounding environment, housing, disease management, primary care etc. (5).
However, it can be critically analysed that healthcare at population level requires the healthcare
professionals and agencies to work collaboratively with the government departments to achieve
the health related goals of the nation (6).
Various health agencies are involved in the collection of data that pertains to health of the
population (7). The use of this data is referred to as secondary health data (8). This aspect is
related to using the personal health information (PHI) of people for purposes which are other
than the direct delivery of healthcare (9). However, the data that has been collected in electronic
medical records can also be put to secondary use (10). This has the potential to result in greater
quality of health care service as well as cost savings (10).
1
Document Page
Secondary uses of healthcare data
Large volumes of data are generated in clinical care which is important for continuity of
care as well as for providing referrals to the patients (11). But it is not always true and it can be
said that along with catering to the needs of the patients, this data also finds its use in generating
alerts to initiate improvement in practice as well as to undertake care activities (11). There are
various secondary purposes for which health data is used (12). These include aspects such as
healthcare management and public health monitoring (12). But, it has been found that Health
system planning is also an important aspect which requires secondary use of health data (13).
Further, another secondary use of health information is in program evaluation (14). So, it can be
concluded that use of personal health information for secondary uses continues to be a debatable
topic. But, for planning and implementation health care at the population level, government
health authorities and other public bodies need to use the data.
Accessibility of data
In today’s times, secondary health data is readily available on internet as well as other
sources such as print materials (15). In order to use the secondary health data for the purpose of
identifying areas for improvement, accessibility is an important factor (16). Convenient
accessibility of the health data aids in accomplishing the research tasks on time. It also increases
its usability for a variety of organizations (17). But I refuse to accept that this easily available
information on internet is accurate source of secondary data. Secondary use of health data can be
ensured by improving its access to the users so it is suppose to be accurate (17). Health data
about the diseases, their prevalence and impact can be accessed on government websites (18).
Various health departments also serve as a source of information which can be put to secondary
use (19). Organizational records can also be accessed for obtaining secondary data and using it
for improving healthcare (20). It can be analysed that secondary health data can thus be used for
influencing healthcare at population level. Accessibility of the data provides convenience to the
healthcare agencies and systems to obtain the relevant data (17, 18). Based on this, the
formulation of health care plans and strategies can be done which improves the delivery of health
care to the population (21). In this way, the secondary health data can be accessed and used (16).
So it can be concluded that this data can only be used for influencing healthcare at population
level, if it can be accessed by the required professional at the required time.
2

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
Further, data about the population is available on the website and records of government
departments (22). This pertains to the health outcomes of specific groups of people, their health
status, causes of deaths, socio- economic parameters regarding health and wellness etc. (22).
However, it has been found that these can be accessed after obtaining the permission from the
respective authority (22). It can be critically analysed that obtaining permission requires further
processes that may consume time and impact the use of health information. But, timely use of the
data is essential for bringing improvements at the larger level (23). However, the process of
accessing data after obtaining permission may lead to delay in accessing the data and using it for
the required purposes, thus decreasing its effectiveness (23).
Cost effectiveness of extracting data
Data extraction is described as the process through which data is obtained from a
database. With the help of this process, subset of the data can be better explored (24). Data
extraction makes the explorations load in a faster manner (25). It can be analysed that data
extraction provides the benefit of allowing the health policies to utilize the information which
could lead to adoption of best methods and enhancement of care (26). This would eventually lead
to decrease in the expenses (27). However, it can be critically analysed that there is lack of
efficient data extraction procedures in the healthcare industry (28). This would negatively
influence the health care at the population level as the process of data extraction will not be cost
effective.
Data extraction may be carried out by instantly extracting the data from the target device.
This is referred to as online extraction (29). However I refuse to accept it and infect on contrary,
offline extraction comprises of extracting data that has been stored somewhere outside (30).
Approved users like doctors can easily obtain data of patients through data extraction (31).
Moreover, the process of data extraction also ensures that the information is kept secure and
contained (32). However, it can be critically analysed that the cost effectiveness of the secondary
use of health data can be increased if there are better ways to extract it. It has been found that
healthcare professionals face a continuous challenge of extracting healthcare data (33). The
reason behind this is the lack of technology integration (33). With the increase in productivity
and demands of workflow, there is a situation where the valuable data remains unseen (33). This
occurs due to lack of data integration and accessibility (34). So, it can be concluded that health
data exists which can be put to secondary use but inability to properly extract it makes it
3
Document Page
inconsequential. It can be analysed that this may negatively influence the health at population
level as the data could not be used for the required purpose.
For the health data to be put to effective use, it is essential for it to move among hospitals,
clinics, devices, electronic health records and other channels which form the basis of modern
health care ecosystem (35). On contrary to view point of this author it can be said that that with
the movement of data, there is a danger of theft and unauthorized access (36). This makes the
healthcare professionals trapped between the concepts of risk and potential (37). In this regard, it
can be evaluated that there is a need to reimagine a way of approaching health data and
extracting it so that now solutions can be forged out (38). It can be analysed that with these
solutions, health care data can be extracted in a better way thus increasing its use for developing
plans regarding healthcare at a population level. So it can be concluded that the cost of extracting
data tends to be equivalent to its utility owing to the lack of appropriate methods of data
extraction. This can be overcome by adopting better processes for data extraction so that it is
convenient and saves time.
Better approaches to data extraction has the potential to radically transform the ways in
advancement of patient care is done (39). This clearly indicates the tremendous influence of
secondary health data on the health at population level (39). Rethinking the approaches to data
extraction and its management will lead to a transformation in the method of conducting clinical
trials as well as improving the quality of care that is received by the service users (40). This will
provide novel solutions for the health at the population level which will benefit larger sections of
people.
Importance of data quality
Data quality is an important aspect particularly in the health care sector (41). The nature
of this data is sensitive and indicates that stringent rules and regulations protect and govern it
(42). Ensuring quality of health data contains a number of benefits which increases its vitality
(42). Data quality provides acceleration to the methods of working of the health care sector
which further boosts its efficiency and accuracy (43). It also leads to improvement in the learning
so that better decision making can be undertaken and new policies and procedures can be
implemented at the population level (43). Furthermore, quality of health data is essential for
enhancing patient safety, when it is to be put to secondary use at the population level (44). High
quality of data and its accuracy will ensure that patient safety is maintained (44). This will not
4
Document Page
only lead to better patient outcomes but also formulation of effective plans at the population
level.
Another reason for the importance of quality of health data is that it is required for
identifying relevant trends and patterns (45). But as compared to other researchers studies it can
be said that when data is to be used for health at population level, it is of crucial importance to
determine the trends and patterns of diseases, their causal factors and prevalence. High quality of
data ensures that all the above aspects are considered (28). When considering the health at the
level of the population, it is required that services are regularly monitored and evaluated (46).
This is needed to highlight the weaknesses prevailing in the various areas of healthcare services
(46). Monitoring and evaluation is also required for building on the strengths of the health care
system of the nation. Therefore, it can be analysed that data quality of health data positively
influences the health at the population level. Furthermore, data quality ensures that better plans
are made for the future. In order to ensure the healthcare services are improved, there is need to
make holistic plans which cater to the needs of the population these plans also need to take care
of the prevailing health patterns and trends (47). So it can be concluded that high quality of data
ensures that health plans are made with a better vision and objective (28). It can be analysed that
in this way, secondary health data can be used to influence healthcare at population level.
Data quality of secondary health data can be ensured by adopting a structured process
(48). All the data that enters the healthcare system through manual entry needs to go through a
process which is appropriately structured. This will ensure that the data can be validated. A clear
structure ensures that risk of inaccurate and poor quality data is checked (49). Another way
through which data quality can be ensured is through formatting and validation (50). When the
data is properly formatted and the structures as well as the processes used are tried and tested, it
helps in preventing errors (49). Thirdly, effective quality of data can be maintained through
security, collaboration and cohesion (51). Proper access and evaluation of data encompasses the
ability to work together. For this, it is required that the various health care professionals,
agencies and health departments collaborate securely and work in a cohesive manner (52).
Continuous monitoring of data and its scalability is another measure to ensure the data quality
(53). This involves monitoring the data on a continuous basis.
Impact of clinical audit in NHS
5

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
It is important to regular audit in health care sector it can be ensured that data and info is
properly stored and managed (54). However other researchers further explains that, audit also
enables in finding out areas where improvement is required. In UK, NHS is public funded
system which offer variety of health care services to people free of cost. The organization is
funded by government. Clinical audit is process to find out whether health care services are
being provided as per set standards or not. Apart from it, conducting of audit also helps in
identifying where service offered to patients is well and where not. Thus, on basis of that weak
areas are identified and then improvement is made. The audit are conducted on regular basis to
ensure that standards and criteria are followed in health care services. Thus, when improvement
is made within process it changes the entire way of giving health care services (55). So it can be
said that, when improvements are made this automatically result in creating a positive impact on
NHS. It is because the process are enhanced and this led to improving quality of health care
services as well. For doing audit in NHS there is a programme which is organized. The NCAPOP
do audit on behalf of NHS. In this total 30 national audits are included. In that, the data is
collected and gathered by local clinicians (56). Thus, it gives a brief overview of how care
standards which are being followed in particular condition.. hence, on local level NHS prepare
report on basis of certain benchmarks and analyse performance. In clinical audit feedback of
patient is also taken by which it becomes easy to identify improvement areas. There are 4 major
program in NCAPOP which help to assess quality of health care. The main thing is to collect
data and ensure that set standards are followed. It is stated that there is positive impact of clinical
audit on NHS. This can be said that in audit standards are checked. So, if they are not being
followed in offering care services then changes are made into it (57). Besides that, some
alternative ways are determined on basis of which improvement can be done. For instance- for
training of staff new process can be applied which brings efficiency in working of staff. So, it
impacts on their productivity in positive way as goals are achieved in less time. When
improvement is made then it results in improving quality of health care services. So, this brings
positive changes in outcomes. Therefore, when outcomes are enhanced then new objectives are
set. The problems or issues are also determined in NHS which are solved. There is auditing of
resources are done to ensure that they are utilised in effective way (58). Hence, this impact on
utilisation of resources in efficient way. In addition to it, clinical audit inspects that how data and
info is stored and maintained. So, if there is any mismanagement in data and info than changes
6
Document Page
are made to keep data secure and private. Therefore, audit impact in positive way on NHS as the
process of sharing data and info is checked. Alongside, there are certain principles as well on
basis of which audit is done. Clinical audit has direct impact on patients where they are mainly
involved in the entire process and it supports a lot in meeting with their expectations (59).
Another impact of audit is that it helps in raising standards and in turn high standards are set for
delivering best healthcare services to the patients. So it can be concluded that clinical audit
assists a lot in enhancing the efficiency as the main aim of the audit is to enhance the quality and
the healthcare firm is able to manage cost in every possible manner. Clinical audit leads to
efficient utilization of the available resources.
But on contrary there is negative impact of clinical audit on NHS as well (60). Here,
when there is require to make improvement in process or service quality then it is highly
complex procedure. This is because it requires overall plan on how change is to be made. Also,
staff resist changes and do not adopt it thus this impact on their performance in negative way. It
is also stated that sometimes changes made are compatible to one area but it does not fit in
another way. Therefore, this also impact on maintaining of quality of health care standards in
negative way. It is also said that there is lack of expertise staff due to which they are not able to
adopt changes and to train them consumes a lot of time. This impact on NHS efficiency in
negative way. Patient expects many things from the new standards and practices in health care
but this creates problems while implementing the plan of auditing (61). This impact on NHS way
of delivering care services.
Principles and techniques of service improvement
The main purpose of service improvement is to bring relevant outcomes. Furthermore, as
per other authors it can be said that it helps in understanding that it enables in finding out weak
areas and improving it. There is continuous efforts made for improving service to make it better
(9). With that it becomes easy to reduce cost as well. So, there are certain principles which is
applied in service improvement. They are explained as below :
Result oriented not process oriented – this is a principle followed which state that task performed
by people is known as specialized task. In this it must be ensured that the improvement made
must be result oriented not process one (6). The changes must emphasis on attaining of outcomes
rather than process. For that it is essential to find out customer requirements.
7
Document Page
Involve people who is in process- this principle state that work must be done by individual to
obtain outcome. Here, all those who are involve in service improvement must be included in it so
that improvement is made (62). Thus, outcome is analyzed and on basis of that improvement is
made into service.
Integrate data collection and process- the principle state that data must be handle and manage by
same person. This is because it will enable in reducing errors in interpreting of data. Hence, the
data gathered is precise by which it can be stated how improvement is made into it.
Share database with interconnect department- this principle state that database must be shared
department which is interconnected. (18) This is because shared database helps in processing of
data and info in proper way. Alongside, there is flexibility in processing of data between
department. Besides, real time access of data is there by quick decisions are taken.
Bridge gap in process- here, the principle state that activities which runs on same process should
be integrated. Thus, common activities are combined together and then process is executed. For
that communication network is made strong. Moreover, coordination between department is
improved and effective process is followed. It results in reducing cost of process as well.
Decision making is part of work- There is need to make effective decisions in service
improvement (41). The principle state that decision making must be a part of work. This means
that it should not be excluded from work and person who is engaged in work must make
decision. Besides, process is enhanced by giving authority to person to make decision. This
enhance speed of work process.
Gather data at origin points- The data must be gathered at point of origin as it eliminates
transmitting info. This also saves times as data has not be gathered at different points.
furthermore, on basis of data real time decision is taken and then it enhances flow of process
(25).
Hence, these all are some principles of service improvement which is followed. It allows
in ensuring that during improvement the standards are applied that makes easy to make changes.
This determine that what changes are to be done and where.
8

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
But on contrary it is stated that principles are not applied in all situations. It is because the
changes in service improvement is not done on principle basis (45). Even if they are applied then
it results in failure of changes by which effective outcomes are not attained. There also occur
error and delay in executing change within service. for example- if decision making principle is
applied then the person decision taken may not be effective. There is also risk of data breach and
quality of data is not maintained. Therefore, this also impact on maintaining of quality of health
care standards in negative way (28). It is also said that there is lack of expertise staff due to
which they are not able to adopt changes and to train them consumes a lot of time.
For services improvement there are certain techniques and principles which are applied.
The use of techniques depends on what type of improvement is to be made. But the principles
followed in it are same. The principles ensure that a specific process or standards are followed in
it by which service improvement is done (35). There are certain features of services which are
there such as intangibility, perishability, variability, etc. Along with that, there are 4 techniques
of it which are defined as follows :
Developing process manual- It is a technique in which there are set of instructions which is to be
carried out day to day. Usually, it is cost effective technique to achieve a standard process. There
is no involvement of human in it. Also, the technique prevents service providers from digressing
from prescribed process steps while delivering services. Moreover, it increases service outcome
as service provider refer to same manual of process. Through this guide exception are managed
in effective way (24).
Automate processes- this technique involve intensive tasks and there is high probability that
variation is infused in process during service delivery. By automating process, it becomes easy to
work in effective way as time taken to deliver is less (40). Besides that, there is less involvement
of people in delivering service. Alongside, productivity of process increases through automation.
This technique eliminates the mistakes which occurs during service delivery.
Reduce failure demand- In this technique the waste within service process is identified. Thus, the
technique is used to find out proportion of failure demand as compare with total demand
received. Apart from it, root cause analysis in order to find out failure of demand and then
relevant actions for that. This technique enables in reducing failure demand of service by which
it becomes easy to fulfil demand on time (35). By finding out main problem it becomes easy to
9
Document Page
eliminate it and also in future it is ensured that the same mistake does not occur again. It is most
common technique that is used in service improvement.
Conduct service blueprint exercise- It is a customer focus approach where service improvement
is done. In this entire map of service journey is developed. Then, the key target customer
segment as well as process which contain service is designed. In addition, user action sequence is
evaluated and touch points or interface which enable service relationship is formed (11). There
are different types of steps which is followed in this technique such as choose of service, identify
goal, stakeholders, conduct blueprint exercise, etc. through that, it present customer role and
value, assist in finding failure points, opportunity for service improvement, etc.
If it is critically analysed than if techniques of service improvement is not effective as it
is not properly applied in that (44). Moreover, the technique is not feasible in all types of service
improvement. There is require to have experience of all types of techniques and how it is
applied. This has resulted in failure of changes in service and obtaining ineffective outcomes
which impact on service delivery as well.
From the above project it has been concluded that secondary health data plays a vital role
in providing valuable information to the health organizations thus enabling them to work for the
betterment of the services. It can directly help in bringing improvement within types of services
of healthcare organization.
10
Document Page
REFERENCES
Books and Journals
1. McColl-Kennedy JR, Zaki M, Lemon KN, Urmetzer F, Neely A. Gaining customer
experience insights that matter. Journal of Service Research. 2019 Feb;22(1):8-26.
2. Mészáros J, Ho CH. Big data and scientific research: the secondary use of personal data
under the research exemption in the GDPR. Hungarian Journal of Legal Studies. 2018
Dec;59(4):403-19.
3. Alamo T, Reina DG, Mammarella M, Abella A. Covid-19: Open-data resources for
monitoring, modeling, and forecasting the epidemic. Electronics. 2020 May;9(5):827.
4. Schuemie MJ, Hripcsak G, Ryan PB, Madigan D, Suchard MA. Empirical confidence
interval calibration for population-level effect estimation studies in observational
healthcare data. Proceedings of the National Academy of Sciences. 2018 Mar
13;115(11):2571-7.
5. van Loggerenberg F, Vorovchenko T, Amirian P. Introduction—Improving Healthcare
with Big Data. InBig Data in Healthcare 2017 (pp. 1-13). Springer, Cham.
6. Hsueh PY, Cheung YK, Dey S, Kim KK, Martin-Sanchez FJ, Petersen SK, Wetter T.
Added value from secondary use of person generated health data in consumer health
informatics. Yearbook of medical informatics. 2017 Aug;26(01):160-71.
7. Gamache R, Kharrazi H, Weiner JP. Public and population health informatics: the
bridging of big data to benefit communities. Yearbook of medical informatics. 2018
Aug;27(01):199-206.
8. Cumyn A, Dault R, Barton A, Cloutier AM, Ethier JF. Citizens, research ethics
committee members and researchers’ attitude toward information and consent for the
secondary use of health data: Implications for research within learning health systems.
Journal of Empirical Research on Human Research Ethics. 2021 Mar
12:1556264621992214.
9. Abdelhamid M, Gaia J, Sanders GL. Putting the focus back on the patient: how privacy
concerns affect personal health information sharing intentions. Journal of medical
Internet research. 2017;19(9):e169.
11

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
10. Xia Q, Sifah EB, Smahi A, Amofa S, Zhang X. BBDS: Blockchain-based data sharing
for electronic medical records in cloud environments. Information. 2017 Jun;8(2):44.
11. Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management,
analysis and future prospects. Journal of Big Data. 2019 Dec;6(1):1-25.
12. Shah SM, Khan RA. Secondary use of electronic health record: Opportunities and
challenges. IEEE Access. 2020 Jul 22;8:136947-65.
13. Kashyap S, Gombar S, Yadlowsky S, Callahan A, Fries J, Pinsky BA, Shah NH. Measure
what matters: counts of hospitalized patients are a better metric for health system
capacity planning for a reopening. Journal of the American Medical Informatics
Association. 2020 Jul;27(7):1026-131.
14. Compagnucci MC, Meszaros J, Minssen T. Nudges or Shoves in the Secondary Use of
Health Data: What is the More Desirable Approach?(Part 2).
15. Kelly JT, Campbell KL, Gong E, Scuffham P. The Internet of Things: Impact and
implications for health care delivery. Journal of medical Internet research.
2020;22(11):e20135.
16. Ramani V, Kumar T, Bracken A, Liyanage M, Ylianttila M. Secure and efficient data
accessibility in blockchain based healthcare systems. In2018 IEEE Global
Communications Conference (GLOBECOM) 2018 Dec 9 (pp. 206-212). IEEE.
17. Wang ZH, Jeng W. Data Accessibility for Biotech and Medicine Industries: A Cross-
stakeholder Perspective. In2018 IEEE International Conference on Industrial Engineering
and Engineering Management (IEEM) 2018 Dec 16 (pp. 1170-1174). IEEE.
18. Lin R, Ye Z, Wang H, Wu B. Chronic diseases and health monitoring big data: A survey.
IEEE reviews in biomedical engineering. 2018 Apr 24;11:275-88.
19. Shah SM, Khan RA. Secondary use of electronic health record: Opportunities and
challenges. IEEE Access. 2020 Jul 22;8:136947-65.
20. Ristevski B, Chen M. Big data analytics in medicine and healthcare. Journal of
integrative bioinformatics. 2018 Sep 1;15(3).
21. Dineen-Griffin S, Garcia-Cardenas V, Williams K, Benrimoj SI. Helping patients help
themselves: a systematic review of self-management support strategies in primary health
care practice. PloS one. 2019 Aug 1;14(8):e0220116.
12
Document Page
22. Nguyen M. The Evolution and Challenges of Government Websites: The Examination on
Transparency and Citizen Engagement (Doctoral dissertation, California State
University, Northridge).
23. Lana RM, Coelho FC, Gomes MF, Cruz OG, Bastos LS, Villela DA, Codeço CT. The
novel coronavirus (SARS-CoV-2) emergency and the role of timely and effective
national health surveillance. Cadernos de saude publica. 2020 Mar 13;36:e00019620.
24. Mathes T, Klaßen P, Pieper D. Frequency of data extraction errors and methods to
increase data extraction quality: a methodological review. BMC medical research
methodology. 2017 Dec;17(1):1-8.
25. Büchter RB, Weise A, Pieper D. Development, testing and use of data extraction forms in
systematic reviews: a review of methodological guidance. BMC medical research
methodology. 2020 Dec;20(1):1-4.
26. Gokhale KM, Chandan JS, Toulis K, Gkoutos G, Tino P, Nirantharakumar K. Data
extraction for epidemiological research (DExtER): a novel tool for automated clinical
epidemiology studies. European journal of epidemiology. 2021 Feb;36(2):165-78.
27. Norman C, Leeflang M, Spijker R, Kanoulas E, Névéol A. A distantly supervised dataset
for automated data extraction from diagnostic studies. InProceedings of the 18th BioNLP
Workshop and Shared Task 2019 Aug (pp. 105-114).
28. Mehta, N. and Pandit, A., 2018. Concurrence of big data analytics and healthcare: A
systematic review. International journal of medical informatics, 114, pp.57-65.
29. Wu AX, Taneja H. How did the data extraction business model come to dominate?
Changes in the web use ecosystem before mobiles surpassed personal computers. The
Information Society. 2019 Oct 20;35(5):272-85.
30. Alam TM, Awan MJ. Domain analysis of information extraction techniques.
International Journal of Multidisciplinary Sciences and Engineering. 2018 Jul;9(6):1-9.
31. Giunti G, Guisado-Fernandez E, Belani H, Lacalle-Remigio JR. Mapping the access of
future doctors to health information technologies training in the European Union: cross-
sectional descriptive study. Journal of medical Internet research. 2019;21(8):e14086.
32. Mushtaq MO, Shahzad F, Tariq MO, Riaz M, Majeed B. An efficient framework for
information security in cloud computing using auditing algorithm shell (AAS). arXiv
preprint arXiv:1702.07140. 2017 Feb 23.
13
Document Page
33. Galetsi P, Katsaliaki K, Kumar S. Values, challenges and future directions of big data
analytics in healthcare: A systematic review. Social science & medicine. 2019 Nov
1;241:112533.
34. Okemiri HA, Rita AU, Isaiah AI. Patient Data Integration: a panacea for effective
healthcare. Journal of Computer Science. 2020;16(2):235-48.
35. Everson J, Adler‐Milstein J. Sharing information electronically with other hospitals is
associated with increased sharing of patients. Health services research. 2020
Feb;55(1):128-35.
36. Osiejewicz J. Education on cyber security issues under European Union law. A standard
of personal data protection. Development of Jurisprudence Problems and Prospects.
2017:73-6.
37. Bernier A, Knoppers BM. Longitudinal Health Studies: Secondary Uses Serving the
Future. Biopreservation and Biobanking. 2021 Jun 25.
38. Leonelli S, Tempini N. Where health and environment meet: the use of invariant
parameters in big data analysis. Synthese. 2021 May;198(10):2485-504.
39. Hulsen T, Jamuar SS, Moody AR, Karnes JH, Varga O, Hedensted S, Spreafico R, Hafler
DA, McKinney EF. From big data to precision medicine. Frontiers in medicine. 2019
Mar 1;6:34.
40. Azir MA, Ahmad KB. Wrapper approaches for web data extraction: A review. In2017
6th International Conference on Electrical Engineering and Informatics (ICEEI) 2017
Nov 25 (pp. 1-6). IEEE.
41. Qadri YA, Nauman A, Zikria YB, Vasilakos AV, Kim SW. The future of healthcare
internet of things: a survey of emerging technologies. IEEE Communications Surveys &
Tutorials. 2020 Feb 11;22(2):1121-67.
42. Beyan O, Choudhury A, van Soest J, Kohlbacher O, Zimmermann L, Stenzhorn H,
Karim MR, Dumontier M, Decker S, da Silva Santos LO, Dekker A. Distributed analytics
on sensitive medical data: The Personal Health Train. Data Intelligence. 2020 Jan 1;2(1-
2):96-107.
43. Shah G, Shah A, Shah M. Panacea of challenges in real-world application of big data
analytics in healthcare sector. Journal of Data, Information and Management. 2019
Dec;1(3):107-16.
14

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
44. World Health Organization. Patient safety: making health care safer. World Health
Organization; 2017.
45. Mantovani M, Amico B, Combi C. Discovering predictive trend-event patterns in
temporal clinical data. InProceedings of the 36th Annual ACM Symposium on Applied
Computing 2021 Mar 22 (pp. 570-579).
46. Godi B, Viswanadham S, Muttipati AS, Samantray OP, Gadiraju SR. E-Healthcare
Monitoring System using IoT with Machine Learning Approaches. In2020 International
Conference on Computer Science, Engineering and Applications (ICCSEA) 2020 Mar 13
(pp. 1-5). IEEE.
47. Fiandaca MS, Mapstone M, Connors E, Jacobson M, Monuki ES, Malik S, Macciardi F,
Federoff HJ. Systems healthcare: a holistic paradigm for tomorrow. BMC systems
biology. 2017 Dec;11(1):1-7.
48. Brundin-Mather R, Soo A, Zuege DJ, Niven DJ, Fiest K, Doig CJ, Zygun D, Boyd JM,
Leigh JP, Bagshaw SM, Stelfox HT. Secondary EMR data for quality improvement and
research: a comparison of manual and electronic data collection from an integrated
critical care electronic medical record system. Journal of critical care. 2018 Oct 1;47:295-
301.
49. Jagadeeswari V, Subramaniyaswamy V, Logesh R, Vijayakumar V. A study on medical
Internet of Things and Big Data in personalized healthcare system. Health information
science and systems. 2018 Dec;6(1):1-20.
50. Razzaghi H, Greenberg J, Bailey LC. Developing a systematic approach to assessing data
quality in secondary use of clinical data based on intended use. 2021.
51. Loewenson R, Accoe K, Bajpai N, Buse K, Abi Deivanayagam T, London L, Méndez
CA, Mirzoev T, Nelson E, Parray AA, Probandari A. Reclaiming comprehensive public
health. BMJ global health. 2020 Sep 1;5(9):e003886.
52. Carlton EL, Singh SR. Joint community health needs assessments as a path for
coordinating community-wide health improvement efforts between hospitals and local
health departments. American journal of public health. 2018 May;108(5):676-82.
53. Prince K, Jones M, Blackwell A, Simpson A, Meakins S, Vuylsteke A. Barriers to the
secondary use of data in critical care. Journal of the Intensive Care Society. 2018
May;19(2):127-31.
15
Document Page
54. Paton C, Adams CE, Dye S, Fagan E, Okocha C, Barnes TR. The pharmacological
management of acute behavioural disturbance: Data from a clinical audit conducted in
UK mental health services. Journal of Psychopharmacology. 2019 Apr;33(4):472-81.
55. Boyle A, Keep J. Clinical audit does not work, is quality improvement any better?.
British Journal of Hospital Medicine. 2018 Sep 2;79(9):508-10.
56. Paton C, Cookson J, Ferrier IN, Bhatti S, Fagan E, Barnes TR. A UK clinical audit
addressing the quality of prescribing of sodium valproate for bipolar disorder in women
of childbearing age. BMJ open. 2018 Apr 1;8(4):e020450.
57. Doherty P, Salman A, Furze G, Dalal HM, Harrison A. Does cardiac rehabilitation meet
minimum standards: an observational study using UK national audit?. Open Heart. 2017
Jan 1;4(1):e000519.
58. Raffe S, Curtis H, Tookey P, Peters H, Freedman A, Gilleece Y. UK national clinical
audit: management of pregnancies in women with HIV. BMC infectious diseases. 2017
Dec;17(1):1-6.
59. Limb C, Fowler A, Gundogan B, Koshy K, Agha R. How to conduct a clinical audit and
quality improvement project. International journal of surgery. Oncology. 2017
Jul;2(6):e24.
60. Rubio MC, Navarrete BA, Soriano JB, Soler-Cataluña JJ, González-Moro JM, Ferrer
ME, Lopez-Campos JL. Clinical audit of COPD in outpatient respiratory clinics in Spain:
the EPOCONSUL study. International journal of chronic obstructive pulmonary disease.
2017;12:417.
61. Jha S, Hillard T, Monga A, Duckett J. National BSUG audit of stress urinary
incontinence surgery in England. International urogynecology journal. 2019
Aug;30(8):1337-41.
62. Foy R, Skrypak M, Alderson S, Ivers NM, McInerney B, Stoddart J, Ingham J, Keenan
D. Revitalising audit and feedback to improve patient care. bmj. 2020 Feb 27;368.
16
Document Page
Project 2
Abstract: this project had focused upon critical analysis of effect that installing test and trace
apps on mobile phones may have on patient privacy and confidentiality, in relation to the
COVID19 pandemic. In this project different ways in which confidentiality and privacy of
patients are hampered have been explained in detailed manner.
Covid-19 has directly impacted whole words in many ways and has brought changes
within ways in services are to be delivered to people. Due to covid 19 there has been complete
change in maintaining individual privacy and data and info. The gov has made changes in how to
treat patient and track their record (15). Also, gov has focused on installing of apps in mobile
phones of people so that relevant data and info is obtained (1). But I refuse to accept this point
and contrary it can be said that it is difficult to accept this because if it help government in
tracking covid-19 patients then it also invades in personal life of people by storing their
information and tracking them without their permissions (3). this has highly affected on privacy
and confidentiality of patient personal data and info (2). It is because the data is now shared with
gov and also third parties, not only this it has also been found that installing of app in mobile
phone is done without any consent of people (3). This means that they have to install app in
phone even when they are not infected with virus. It is done to ensure that other people do not
comes in contact with infected ones (19). Furthermore, without any consent of individual the app
is installed in their phone and besides that, it is found that tracking and tracking of app has led to
breach of confidentiality of patient personal info (1). This clearly helps in concluding that,
installing government application is beneficial for government as they can track movement of
Covid-19 patients but for people it is highly unethical because they are constantly been tracked,
without their consent their information is been saved. Which as per one’s own point of view is
unethical. when a patient go out or do something then it is easily tracked with help of app (3).
Furthermore, personal data of patient regarding their age, gender, address, contact no. etc. is
entered into app (2). Along with that, there is no assurance given by gov as well if the data and
info is protected or secured (15). It is because the app data may be maintained by third party and
as result confidentiality of patient can be protected by protecting data in effective way because
data can be stored and managed properly (15). But there are very less number of case studies that
support this argument.
17

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
The app reveal about someone daily living as when it traces and track location of
individual it clearly shows that where patient is going, what he or she is doing, etc. (22) Also,
within particular time person has to share details of health status (1). It is important to store
and protect data and info in effective way so that it is not breached (4). So, for that there is need
to have strong server on which data is stored and moreover, only authorized person must be
allowed to access data (25). If it is critically analysed then in order to gain information of people
who are infected their personal data storage is important but if it is not stored then users of
application are asked to store and save their personal information (22). This data is owned by a
third party and if it is hacked then it can directly affect users of application as their privacy might
get breached. There should be firewall and specific protocols and standards followed and
maintained to protect data this will help in ensuring that data must be stored and protected (5).
Basically, the data is owned by third party who has develop app not by gov (6). So, third party is
fully accountable and responsible for ownership of data. The data gathered is not highly secured
(22). The data breach can easily occur if hacker tries to steal data (7). Also, data size is very large
so it becomes difficult to secure and protect it properly because there is no authentic proof that
data is shared but it might be possible that it is shared with hospitals to ensure patient safety (8).
The data is secured in effective way in centralize server but security standards are not high (9). It
is important to enter all details of person who is infected but if the info of infected person is not
entered in app then it breaches duty of care (10). Therefore, no action is taken in regard with it
(4). This further raise concern related to duty of care for which application has been developed.
In terms of legal aspect, it is stated that installation of app does contravene data and
protection law (7). In that it is found that UK data protection act 2018 state that all data
protection principle is to be followed (11). The info must be used in fair, lawful and transparent
manner (5). There are strict rules which must be followed (4). But when app is installed in
mobile phones it led to tracking and tracing of info of patient (20). Besides, the confidential data
is obtained from app and many times this data is not used in fair and transparent manner (12).
Hence, legal aspect is not followed in it due to which data confidentiality is not maintained and
additionally, in context of ethical aspect it is found that there is need to control disease against
individual right to privacy (13). It is because saving human lives is important than breach of data
and info and furthermore, it is priority of gov to prevent spread of disease and save life of people
(1). But right to privacy has to be maintained as well in it and the confidential and personal data
18
Document Page
and info of people should be maintained (14). This ethics of moral framework has to be followed
in it and for ownership of data it must be owned by gov (16). They should not give any authority
to third party or any organization for it (20).
Development of health informatics profession
In health care sector there is wide variety of data and info that is to be managed. So, it
requires expert and professional to manage in database with use of system and server (17). But as
per other authors health info consists of many things like patient medical history, age, gender,
address, etc. (22) Thus, in order to effectively manage data there is need of experts and
professionals (17). It resulted in development of health informatics professionals (18). They use
their knowledge of healthcare, info systems, databases and IT security to gather, store, interpret
and manage the massive amount of data generated when care is provided to patients
(22). Furthermore, they provide data driven solutions as well to improve patient health (19). The
development of health informatics occurred in 1970. Here, first electronic medical record was
developed (18). Then, in late 1970, biometric record was introduced in which data related to
DNA, etc. were stored (17). Then, gov and other organization worked together to create a
framework and format of how to store patient registration details, discharge, order, etc. and in the
1970s and 1980s, computer became small and portable (19). The desktops and laptops were used
in hospitals and clinics. The new programs such as patient scheduling and automated order entry
were developed and used (21). Besides, federal gov gave $1 billion to Science Applications
International Corp in order to develop the first computerized healthcare system for the
Department of Defense (25). In order to work in health informatics profession there requires
several skills which are problem solving, interpersonal, communication, etc. these allow person
to handle and manage data and info is appropriate way (23) It is further evaluated and compared
with findings of other researchers then it can be said that health informatics has highly benefited
in storing of data and info (15). Besides that, use of computers and robotics is useful in treating
of disease and providing treatment to patients (19). The experts and professionals are able to use
and maintain clinical and other info is proper way (24). There is no doubt that this data is used
for providing more accurate and effective care to patients but this conclusion cannot be accepted
because various studies have shown that this data is no only used for providing high quality
patient care but many times it is used other purposes as well that help hospitals in analysing and
19
Document Page
evaluating their patients, needs etc. many times without their patients concept their data is being
used. Also on contrary if it is critically evaluated then it is critiqued that health informatics
profession is not beneficial (24). This is because it requires a lot of in depth and advance skills
by which data and info is managed (23). Besides that, sometimes even when data is managed and
stored in proper way there occurs data breach (19). So, it led to misuse of data (19). Moreover,
the info obtained of patient from database is not useful and irrelevant (28). This results in
ineffective sharing of data and info of patient (25). Then, treatment provided is also not proper
and this led to certain consequences (26). It is also evaluated that there are no specific standards
which is followed in this profession to manage data and info (27). Each health informatics uses
varied data framework and structure to store and manage data (27). So, when data is shared then
it is not properly structured (25). The format has to be modified in it (17). Apart from it, skills
and knowledge is considered as necessary in health informatics profession (8). This is because it
person is not having sufficient knowledge of data informatics and this led to breach of data and
info (1). So it can be concluded from this evidence and discussion that technological
advancement there are several changes occurring in health informatics (5). So, it creates
challenge in front of profession that how to overcome those and maintain integrity of system
(10).
Issues affecting individual privacy and maintenance of confidentiality
Data confidentiality refers to maintaining of data and info from any unauthorized,
unintentional and unlawful access of data (29). Whereas confidentiality is related to privacy of
info. and to share, view and use it (25). The use of app to trace and track data of patient has led
to rise in various types of issues (31). The issues directly affect on individual privacy of info and
maintaining of confidentiality (30). So, it is important to find out those issues so that relevant
action and measures are taken to solve it (29). Hence, they are discussed as below:
Mishandling of data- it is most common issue which affect individual privacy as in healthcare,
data and info of patient who are registered on app is mishandled which means that info is not
properly stored and managed (31). Along with that, due to network error or failure the data is not
properly shared or transferred that result in ineffective storing of data and info and sometimes,
there also occurs a situation where expert is not having relevant skills and knowledge on how to
manage and handle data (32). this result in data privacy as it is shared with third party or any
20

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
other person. Here, info is mishandled by person for different purposes and is not encrypted and
is used by any individual (28).
Location tracking – It is also issue in privacy where it is found that the main thing is location of
individual in tracked and traced (36). Here, the data and info of patient is tracked and then it is
stored (33). Sometime, the location of patient is tracked and then the data is stored in system
(35). The data consists of various things like name, address, etc. of individual (34).
Trust- This issue occurs in confidentiality of data where professionals does not treat info as
confidential (39). They may require to withhold that info to treatment (37). Hence, they disregard
the privacy of client and personal info (38). By that the privacy of patient is not maintained and
they are not able to protect and secure data in proper way (40).
It is critically evaluated that in reality, the complete maintenance of confidentiality is
not always possible and there exist several exception (33). The issues of privacy and
confidentiality does not always occur in storing data and info (35). they only occur when there is
critical situation or any technical error or fault (32). By that it results in issue sin privacy and part
from that, the issue also happens with human error by which it led to data breach (36). The
privacy and confidentiality of data is maintained may be hacked by hacker (26). So, in this there
is no issue if privacy and now with help of advance security system and software it has become
easy to protect and secure data in effective way (40). This enables in protecting data in secure
way by which ensure that confidentiality is maintained (22). Besides that, there are certain
policies and procedure by which issues are resolved (39). There are strict laws and regulations
which is formed by organization to ensure that privacy issues does not occur (36). Here, for gov
it is important to prevent spread of virus rather than focusing on privacy issues (37). It helps in
concluding that main purpose of gov as well. However, many privacy issues occur due to
technical and human error (38). They also led to privacy and confidentiality of data and info.
(40)
Critical awareness of current legislation on data protection
It is important to store and protect data and info in effective way so that it is not breached
(42). So, for that there is need to have strong server on which data is stored (47). But on contrary
as per view points of authors it can be said that, only authorized person must be allowed to
21
Document Page
access data and it is important to protect and secure data so that it is not misused (43). Along
with it, in every nation there are certain laws and regulations as well which need to be followed
(42). The laws provide a framework on how to protect and manage data and besides that, the law
is to be adhered by all companies as well as individuals (44). They are being formed by gov and
it varies in different countries but however, in UK there is also legislation which is formed on
data protection (45). Here, data protection act 2019 is passed by UK parliament (42). The act is
based on general data protection regulation (GDPR) (42). It states how personal info is to be
used by organizations and gov (46). If it is critically analysed then it can be states that each
individual is responsible for using personal data and info and has to follow strict rules which are
known as data protection principles (47). The act consists of several principles which are as
follows :
the data is to be used in fair, lawful and transparent way (48)
The data must be used for specified and explicit purpose (48).
It must be used in adequate and only in limited way that what is necessary (48).
Data must be accurate and must be kept up to date where it is necessary (48).
It must not be kept longer than necessary (48).
Data should be handled in such a way that it ensures security and protect against unlawful
and unauthorized access, loss or damage (48).
Besides, there is strong legal protection for sensitive data that relate to race, sex life, genetics,
political opinions, ethnic background, etc. (45)
Moreover, there are different principles in regard with use of personal data of criminal and
offences (48).
Apart from it, there are certain rights as well of individual in data protection act which state what
is right of individual to find out info from gov and other businesses (48). They are as follows :
To be informed about how data is being used (49).
To access personal data (49).
To have incorrect data updated (49).
To stop processing of data (49).
To object how data is processed in some situations (49).
22
Document Page
To engage in decision making process if organization uses personal data and info (49).
Until Brexit occurred UK was a part of EU so they have to follow GDPR legislation (50). It
stated that all members of EU have to follow GDPR guidelines (50). So, UK gov pass data
protection act 2018 and it covered the EU law as well (51). The law is aligned with GDPR in
certain ways and in that certain provision is considered between supervisor bodies and EU
member state (52). In that power help is by UK info commissioner, furthermore, in UK the
information commissioner office is responsible for enforcing UK GDPR law (53). It is a body
that is sponsor by digital department, culture, media, etc. and it report directly to parliament
(51). The UK GDPR apply to businesses operating outside UK territory and it also apply to
resident as well (52). There are several rights of individual which is laid down in data protection
law (50). Critical analysis can further help in ensuing that data and info is controlled and
managed in effective way the Data protection officer is appointed who monitor overall process
(54). It is important to be ensured that data is protected by data protection officer as it can
directly help in ensuing that data of customers is protected. Not doing so can put data of
customers at risk. This is done in large scale regular and systematic monitoring of individual,
processing of large scale personal data (55). if the data protection laws are not complied then it
led to several penalties as well (56).
From this critical analysis it can be concluded that in UK data protection act 2018 is only
law which is being followed in protecting data and info (47). but this law is not enough to protect
data as it only outlines principles and rights of individuals (48). But it also requires to set up
certain responsibilities of various agencies as well so that the personal data and info is used in
proper way (47) It will enable in controlling the process that how data is used by business for
their personal use (48). So, this act does not specify duties and roles agencies which is
responsible for monitoring all businesses (49). Thus, it requires more guidelines and framework
on how data is to be used by business, etc. (47)
Assess ethical and social consideration of health informatics
Health informatics has emerged as new concept in which the data and info is stored
electronically (57). The profession allows individual to ensure that data is managed and stored in
proper way (58). But if it is critically analysed then there are certain ethical and legal issues
which can also occur in health informatics (57). The issues need to be identified so that it can be
23

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
solved in effective way (59). Moreover, the issues can occur in social and ethical (60). This
highly impact on manage and control of data and info. thus, they are as below : The code of
ethics and standards explain that ethical obligation include safeguarding privacy and security of
health info. and to maintain health info system (61). Apart from it, ethics also state ensuring
accessibility and integrity of health info (58). This helps in concluding that it is important to have
certain ethics that should be considered in health informatics. Besides that, there are certain other
ethics as well which is to be considered in health informatics (57). So, they are defined as:
Promoting of high standards of health info management practice (62).
To identify the core values of the health info management (62).
Summary of ethical principles which shows core values (62).
Forming of ethical principles used to direct decisions and actions (62).
Forming of a framework for professional resolution of conflicts and ethical uncertainties
(62).
Providing ethical principles which enable public to hold health info management
professionals responsible (62).
To provide opportunities for mentors to guide new practitioners in ethics education (62).
There are several social considerations as well which are followed in health informatics (53).
In that it is found that improving functions is to find out what are improved functions (57). This
require to support effective working on health sector to show enables services 24/7 (60). So, it
requires to provide staff adequate training so that staff can work to implement changes and
hence, it requires to ensure that social norms, ethics, values, etc. are followed in maintaining
health info (62). Hence, no data and info must be stored which is against the ethics, values, etc.
of social consideration of patient (61). Therefore, all norms and guidelines must be followed in
social health and care and alongside, another social consideration is that to manage health care
provision as an efficient business (60). But this argument cannot be accepted completely because
taking into considerations of all ethical and social norms of health and information because when
delivery of quality care come into picture then it becomes important to store information of
patients. But still planning and clinical conditions has to be as per social norms and ethics (58).
The health informatics profession must work as per social ethics and he or she should follow it
and maintain it as well (58). If there occurs any situation where ethics is not followed then social
24
Document Page
consideration is to be applied (57). The patient notes must be complete, consistent, comparable,
contextualised and contemporary (58). The knowledge base of general published evidence about
health issues similarly needs to be readily accessible; in a form which gets appropriate
information to the right person, at the right time and in an appropriate form for use (59).
Evaluate impact of current UK and international ehealth strategies
Basically, ehealth strategy focus on improving the system to provide health care services
to people (63). The strategy is developed by which gov, health care providers, consumers, etc.
are involved (64). There is a proper framework by which the health care strategy is developed. In
ehealth system, measure, applications, etc. It is an approach taken by entity in context of health
care sector (65). In similar way in UK, ehealth strategy is developed; the national ehealth policy
is formed which is responsible for coordinating performance and progress of national health
service bodies (58). The UK health department has developed a programme for IT (NPfIT) in
UK (66). The NPfIT was launched in 2002 and is largest public sector health IT projects in the
world and aims to provide authorised access to patient info when needed (67). The main purpose
is to install an integrated IT infra and system for all NHS organisatio, that allow patients to make
informed health choices and it enhance efficiency and effectiveness of clinicians and other NHS
staff (66). The main purpose of this programme is to :
To create a NHS Care Records Service to improve the sharing of consenting patients
records across the NHS and also provide patient access to their own health records (67).
To make it easy and fast for GP and other primary care staff to book hospital
appointments for patients (67).
To provide a system for electronic transmission of prescriptions (67).
To ensure a secure broadband network infra is in place to connect all NHS bodies in UK
(67).
To create picture archiving and communications system (67).
There is a great impact of ehealth strategies on UK and in that it is found that the strategy has
focused on sustainability of health system (68). The main purpose is to access health info easily
so that health care services is offered quickly (668). This has resulted in delivery of care services
in wide area (68). Alongside, the info is available in effective way that include past medical
history (69). Moreover, by developing an integrated IT infra it has resulted in increasing
25
Document Page
efficiency of health. It can be critically evaluated with the help of research that all the resources
are required to be allocated in an effective manner (69). All local and state health center works in
integrated way by sharing of info (70). the integrated health infra is used in systematic delivery
of services (70). This has also resulted in decreasing cost of accessing health services by people
(71). They are able to get high quality services at low cost (71). Therefore, it has resulted in
enhancing health outcomes and also improving health of people (72). The lifestyle of people has
enhanced as well (70). It can also be concluded that along with it, the use of technology, AI,
software and applications, etc. in health care has enhanced the outcomes of health (72). The
patient needs are easily being fulfilled (71). The health network has strengthened and more
digital health interventions are taken (72). Also, people are given safe and secure health care
services. However, at international level as well there are some ehealth strategy that is
developed (73). The agenda for sustainable development and global connection of technology
(74). Till 2030 the main aim is to bridge digital gap and develop knowledge societies (75). Here,
health related sustainable development goals are main to be achieved (73). The strategy is
developed by WHO and UNGA (76). The focus is on continue cooperation of stakeholder of
both developed and developing nations (73). Alongside, resources are allocated in relevant way
as per progress in health care sector. There is digital transformation which is done in effective
way (75).
There is positive impact of ehealth strategy as it has enabled in sharing of data and info
with other nations (77). Besides that, all stakeholders are engaged by which it is easy to find out
what are health care needs (41). Thus, on basis of that resources are allocated and shared with
them. Apart from it, the focus is on person centered care and giving support to professionals
(81). As per a case study it has been critically analysed those various developing nations are
getting support from develop countries (81). Thus, they are able to use resources in efficient
way(78). Furthermore, integrated heath infra has led to sharing of info and communicating it
quickly (78). Along with it, the use of technology, AI, software and applications, etc. in health
care has enhanced the outcomes of health (43). Thus it is concluded that in decreasing cost of
accessing health services by people (79). They are able to get high quality services at low cost
and the patient needs are easily being fulfilled (80). The health network has strengthened and
more digital health interventions are taken (80). Also, people are given safe and secure health
care services. Hence, there is improvement in overall health of people and their lifestyle (81).
26

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
From the above project it has been concluded that in order to bring improvement within
health of people and keep a track of health of Covid positive patients government has developed
an application that keeps a track of patients. But it has resulted in comptonization in privacy and
confidentiality of patients. However various strategies such as ehealth strategy have been
developed that has made sharing of data much easier.
27
Document Page
REFERENCES
Books and Journals
1. Drew DA, Nguyen LH, Steves CJ, Menni C, Freydin M, Varsavsky T, Sudre CH,
Cardoso MJ, Ourselin S, Wolf J, Spector TD. Rapid implementation of mobile
technology for real-time epidemiology of COVID-19. Science. 2020 Jun
19;368(6497):1362-7.
2. Thorneloe R, Epton T, Fynn W, Daly M, Stanulewicz N, Kassianos A, Shorter GW, Moll
SJ, Campbell M, Sodergren S, Chapman S. Scoping review of mobile phone app uptake
and engagement to inform digital contact tracing tools for COVID-19.
3. Urbaczewski A, Lee YJ. Information technology and the pandemic: A preliminary
multinational analysis of the impact of mobile tracking technology on the COVID-19
contagion control. European Journal of Information Systems. 2020 Jul 3;29(4):405-14.
4. Kim W, Lee H, Chung YD. Safe contact tracing for COVID-19: A method without
privacy breach using functional encryption techniques based-on spatio-temporal
trajectory data. PloS one. 2020 Dec 11;15(12):e0242758.
5. Barona R, Anita EM. A survey on data breach challenges in cloud computing security:
Issues and threats. In2017 International Conference on Circuit, Power and Computing
Technologies (ICCPCT) 2017 Apr 20 (pp. 1-8). IEEE.
6. Finnigan P. Data Breach. InOracle Incident Response and Forensics 2018 (pp. 1-25).
Apress, Berkeley, CA.
7. Solove DJ, Citron DK. Risk and anxiety: A theory of data-breach harms. Tex. L. Rev..
2017;96:737.
8. Galetsi P, Katsaliaki K, Kumar S. Values, challenges and future directions of big data
analytics in healthcare: A systematic review. Social science & medicine. 2019 Nov
1;241:112533
9. Manworren N, Letwat J, Daily O. Why you should care about the Target data breach.
Business Horizons. 2016 May 1;59(3):257-66.
10. Elias J. Course Correction—Data Breach as Invasion of Privacy. Baylor Law Review.
2017;69(4).
28
Document Page
11. Bailey J. Data protection in UK library and information services: Are we ready for
GDPR?. Legal Information Management. 2018 Mar;18(1):28-34.
12. Symer MM, Abelson JS, Milsom J, McClure B, Yeo HL. A mobile health application to
track patients after gastrointestinal surgery: results from a pilot study. Journal of
Gastrointestinal Surgery. 2017 Sep;21(9):1500-5.
13. Vokinger KN, Nittas V, Witt CM, Fabrikant SI, Von Wyl V. Digital health and the
COVID-19 epidemic: an assessment framework for apps from an epidemiological and
legal perspective. Swiss Medical Weekly. 2020.
14. Wang T, Duong TD, Chen CC. Intention to disclose personal information via mobile
applications: A privacy calculus perspective. International journal of information
management. 2016 Aug 1;36(4):531-42.
15. Kelly JT, Campbell KL, Gong E, Scuffham P. The Internet of Things: Impact and
implications for health care delivery. Journal of medical Internet research.
2020;22(11):e20135.
16. Fotopoulou A. From networked to quantified self: Self-tracking and the moral economy
of data sharing. InA networked self and platforms, stories, connections 2018 May 24 (pp.
144-159). Routledge.
17. Alonso SG, de la Torre Diez I, Rodrigues JJ, Hamrioui S, Lopez-Coronado M. A
systematic review of techniques and sources of big data in the healthcare sector. Journal
of medical systems. 2017 Nov;41(11):1-9.
18. Bahri S, Zoghlami N, Abed M, Tavares JM. Big data for healthcare: A survey. IEEE
access. 2018 Dec 21;7:7397-408.
19. Lv Z, Qiao L. Analysis of healthcare big data. Future Generation Computer Systems.
2020 Aug 1;109:103-10.
20. Chrysler A, Warnars HL, Utomo WH. Mobile application to track people in covid19
monitoring and patients under covid19 supervision. InIOP Conference Series: Earth and
Environmental Science 2021 Apr 1 (Vol. 729, No. 1, p. 012032). IOP Publishing.
21. AREF MH, SHARAWI A. Centralized Medical Gas Monitoring Solution For Medical
Piping Gases In The Hospitals.
29

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
22. Nguyen M. The Evolution and Challenges of Government Websites: The Examination on
Transparency and Citizen Engagement (Doctoral dissertation, California State University,
Northridge).
23. Meyer MA. Healthcare data scientist qualifications, skills, and job focus: a content
analysis of job postings. Journal of the American Medical Informatics Association. 2019
May;26(5):383-91.
24. Stanfill MH, Marc DT. Health information management: implications of artificial
intelligence on healthcare data and information management. Yearbook of medical
informatics. 2019 Aug;28(01):056-64.
25. Büchter RB, Weise A, Pieper D. Development, testing and use of data extraction forms in
systematic reviews: a review of methodological guidance. BMC medical research
methodology. 2020 Dec;20(1):1-4.
26. Fife CE, Eckert KA. Harnessing electronic healthcare data for wound care research:
standards for reporting observational registry data obtained directly from electronic
health records. Wound Repair and Regeneration. 2017 Apr;25(2):192-209.
27. Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management,
analysis and future prospects. Journal of Big Data. 2019 Dec;6(1):1-25.
28. Olaronke I, Oluwaseun O. Big data in healthcare: Prospects, challenges and resolutions.
In2016 Future technologies conference (FTC) 2016 Dec 6 (pp. 1152-1157). IEEE.
29. Witt MM, Witt MP. Privacy and confidentiality measures in genetic testing and
counselling: arguing on genetic exceptionalism again?. Journal of applied genetics. 2016
Nov;57(4):483-5.
30. Mosquera I. Privacy and confidentiality in exchange of information procedures: some
uncertainties, many issues, but few solutions. Intertax. 2017 May 1;45(5).
31. Newman, R., 2020. Privacy and confidentiality: Issues in psychoanalysis in the 90s.
In Psychoanalytic Therapy As Health Care (pp. 119-123). Routledge.
32. Forrest L, Elman NS, Bodner KE, Kaslow NJ. Trainee confidentiality: Confusions,
complexities, consequences, and possibilities. Training and Education in Professional
Psychology. 2021 Apr 1.
33. Frith J, Saker M. <? covid19?> It Is All About Location: Smartphones and Tracking the
Spread of COVID-19. Social Media+ Society. 2020 Jul;6(3):2056305120948257.
30
Document Page
34. Alepis E, Patsakis C. There's Wally! Location Tracking in Android without Permissions.
InICISSP 2017 Feb 19 (pp. 278-284).
35. Hong, B., Bae, S. and Kim, Y., 2018, February. GUTI Reallocation Demystified: Cellular
Location Tracking with Changing Temporary Identifier. In NDSS.
36. Kawakami S, Sakamoto S, Okamoto S. A Location-Tracking Method With a
Convolutional Neural Network. International Journal of Mobile Computing and
Multimedia Communications (IJMCMC). 2021 Jul 1;12(3):17-26.
37. Zhang P, Zhou M, Fortino G. Security and trust issues in Fog computing: A survey.
Future Generation Computer Systems. 2018 Nov 1;88:16-27.
38. Gugnani G, Ghrera SP, Gupta PK, Malekian R, Maharaj BT. Implementing DNA
encryption technique in web services to embed confidentiality in cloud. InProceedings of
the Second International Conference on Computer and Communication Technologies
2016 (pp. 407-415). Springer, New Delhi.
39. Petrova E, Dewing J, Camilleri M. Confidentiality in participatory research: Challenges
from one study. Nursing ethics. 2016 Jun;23(4):442-54.
40. Rajarajeswari S, Somasundaram K. Data confidentiality and privacy in cloud computing.
Indian Journal of Science and Technology. 2016 Jan 19;9(4):1-8.
41. Custers B, Dechesne F, Sears AM, Tani T, van der Hof S. A comparison of data
protection legislation and policies across the EU. Computer Law & Security Review.
2018 Apr 1;34(2):234-43.
42. Custers B, Dechesne F, Sears AM, Tani T, van der Hof S. A comparison of data
protection legislation and policies across the EU. Computer Law & Security Review.
2018 Apr 1;34(2):234-43.
43. De Hert P, Papakonstantinou V. The rich UK contribution to the field of EU data
protection: Let's not go for “third country” status after Brexit. Computer law & security
review. 2017 Jun 1;33(3):354-60.
44. Floridi L. Soft ethics, the governance of the digital and the General Data Protection
Regulation. Philosophical Transactions of the Royal Society A: Mathematical, Physical
and Engineering Sciences. 2018 Nov 28;376(2133):20180081.
31
Document Page
45. Dove ES. The EU General Data Protection Regulation: implications for international
scientific research in the digital era. Journal of Law, Medicine & Ethics.
2018;46(4):1013-30.
46. van Loenen B, Kulk S, Ploeger H. Data protection legislation: A very hungry caterpillar:
The case of mapping data in the European Union. Government Information Quarterly.
2016 Apr 1;33(2):338-45.
47. Carey, P., 2018. Data protection: a practical guide to UK and EU law. Oxford
University Press, Inc..
48. Spencer A, Patel S. Applying the data protection act 2018 and general data protection
regulation principles in healthcare settings. Nursing Management. 2019 Jan 28;26(1).
49. Bieker F, Friedewald M, Hansen M, Obersteller H, Rost M. A process for data protection
impact assessment under the european general data protection regulation. InAnnual
Privacy Forum 2016 Sep 7 (pp. 21-37). Springer, Cham.
50. Rowley P. Navigating Brexit and GDPR. ITNOW. 2016 Dec 1;58(4):48-9.
51. Phillips AM, Hervey TK. Brexit and biobanking: GDPR perspectives. InGDPR and
Biobanking 2021 (pp. 145-183). Springer, Cham.
52. Murray AD. Data transfers between the EU and UK post Brexit?. International Data
Privacy Law. 2017 Aug 1;7(3):149-64.
53. Woods L. United Kingdom: Heading towards Brexit but with a Data Protection Bill
Implementing GDPR. Eur. Data Prot. L. Rev.. 2017;3:500.
54. Lambert P. The Data Protection Officer: Profession, Rules, and Role. CRC Press; 2016
Nov 25.
55. Bhaimia S. The general data protection regulation: the next generation of EU data
protection. Legal Information Management. 2018 Mar;18(1):21-8.
56. Team IG. Eu general data protection regulation (gdpr)–an implementation and
compliance guide. IT Governance Ltd; 2020 Oct 15.
57. Ammenwerth E, Rigby M, editors. Evidence-based health informatics: Promoting safety
and efficiency through scientific methods and ethical policy. IOS press; 2016 May 20.
58. Fernandez-Luque L, Kushniruk AW, Georgiou A, Basu A, Petersen C, Ronquillo C,
Paton C, Nøhr C, Kuziemsky CE, Alhuwail D, Skiba D. Evidence-based health
32

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
informatics as the foundation for the COVID-19 response: a joint call for action. Methods
of information in medicine. 2021 May 11.
59. Aitken M, Cunningham-Burley S, Pagliari C. Moving from trust to trustworthiness:
Experiences of public engagement in the Scottish Health Informatics Programme.
Science and Public Policy. 2016 Oct 1;43(5):713-23.
60. Ienca M, Ferretti A, Hurst S, Puhan M, Lovis C, Vayena E. Considerations for ethics
review of big data health research: A scoping review. PloS one. 2018 Oct
11;13(10):e0204937.
61. Harman LB, Cornelius F, editors. Ethical health informatics: Challenges and
opportunities. Jones & Bartlett Publishers; 2017.
62. Petersen C, Subbian V. Special Section on Ethics in Health Informatics. Yearbook of
Medical Informatics. 2020 Aug;29(01):077-80.
63. Grady A, Yoong S, Sutherland R, Lee H, Nathan N, Wolfenden L. Improving the public
health impact of eHealth and mHealth interventions. Australian and New Zealand journal
of public health. 2018 Jan 31;42(2).
64. Currie CL, Larouche R, Voss ML, Higa EK, Spiwak R, Scott D, Tallow T. The impact of
eHealth group interventions on the mental, behavioral, and physical health of adults: a
systematic review protocol. Systematic reviews. 2020 Dec;9(1):1-6.
65. Jeminiwa R, Hohmann L, Qian J, Garza K, Hansen R, Fox BI. Impact of eHealth on
medication adherence among patients with asthma: A systematic review and meta-
analysis. Respiratory medicine. 2019 Mar 1;149:59-68.
66. Hale TM, Chou WY, Cotten SR, editors. eHealth: Current Evidence, Promises, Perils,
and Future Directions.
67. Justinia T. The UK's National Programme for IT: Why was it dismantled?. Health
services management research. 2017 Feb;30(1):2-9.
68. De Rosis S, Nuti S. Public strategies for improving eHealth integration and long‐term
sustainability in public health care systems: Findings from an Italian case study. The
International journal of health planning and management. 2018 Jan;33(1):e131-52.
69. Jackson BD, Gray K, Knowles SR, De Cruz P. EHealth technologies in inflammatory
bowel disease: a systematic review. Journal of Crohn's and Colitis. 2016 Sep
1;10(9):1103-21.
33
Document Page
70. Poritska A, Kravets R, Tymovchak-Maksymets O. E-Commerce and E-Health Strategies
and Implementation Activities in the United Kingdom.
71. Grady A, Yoong S, Sutherland R, Lee H, Nathan N, Wolfenden L. Improving the public
health impact of eHealth and mHealth interventions. Australian and New Zealand journal
of public health. 2018 Jan 31;42(2).
72. Avery P. Developments and challenges of e-health strategies for people with
inflammatory bowel disease. British Journal of Healthcare Management. 2020 Mar
2;26(3):73-6.
73. Ammenwerth E, Duftschmid G, Al-Hamdan Z, Bawadi H, Cheung NT, Cho KH,
Goldfarb G, Gülkesen KH, Harel N, Kimura M, Kırca Ö. International comparison of six
basic eHealth indicators across 14 countries: an eHealth benchmarking study. Methods of
Information in Medicine. 2020 Dec;59(S 02):e46-63.
74. Ahonen O, Kouri P, Kinnunen UM, Junttila K, Liljamo P, Arifulla D, Saranto K. The
Development Process of eHealth Strategy for Nurses in Finland. InNursing informatics
2016 Jan 1 (pp. 203-207).
75. Alunyu AE, Nabukenya J. A Conceptual Model for Adaptation of eHealth Standards by
Low and Middle-Income Countries. Journal of Health Informatics in Africa. 2018 Nov
24;5(2).
76. Mousavi SM, Takian A, Tara M. Design and validity of a questionnaire to assess national
eHealth architecture (NEHA): a study protocol. BMJ open. 2018 Dec 1;8(12):e022885.
77. Dumit EM, Novillo-Ortiz D, Contreras M, Velandia M, Danovaro-Holliday MC. The use
of eHealth with immunizations: An overview of systematic reviews. Vaccine. 2018 Dec
18;36(52):7923-8.
78. Njoroge M, Zurovac D, Ogara EA, Chuma J, Kirigia D. Assessing the feasibility of
eHealth and mHealth: a systematic review and analysis of initiatives implemented in
Kenya. BMC research notes. 2017 Dec;10(1):1-1.
79. Aardoom JJ, Loheide-Niesmann L, Ossebaard HC, Riper H. Effectiveness of eHealth
interventions in improving treatment adherence for adults with obstructive sleep apnea:
meta-analytic review. Journal of medical Internet research. 2020;22(2):e16972.
34
Document Page
80. Herrero RG, Ortiz AC, Ruiz AV, Spector C, Carreño G, Guerra JA, Rolón FE, Laurenza
MI, Maffini MM, Carvajal P, Pinchetti RF. The Impact of eHealth on Patient Safety.
eHealth Conversations. 2016:282.
81. Slev VN, Mistiaen P, Pasman HR, Verdonck-de Leeuw IM, van Uden-Kraan CF,
Francke AL. Effects of eHealth for patients and informal caregivers confronted with
cancer: a meta-review. International journal of medical informatics. 2016 Mar 1;87:54-
67.
35
1 out of 37
circle_padding
hide_on_mobile
zoom_out_icon
[object Object]

Your All-in-One AI-Powered Toolkit for Academic Success.

Available 24*7 on WhatsApp / Email

[object Object]