2. Citation: Osman MAA (2018) A Prospective Medical System of the Future/a Complete Health Care System. J Gen Pract 6: 371.
Page 2 of 4
Volume 6 • Issue 4 • 1000371
J Gen Pract, an open access journal
ISSN: 2329-9126
The Design
Aprospectivemedicalsystemofthefutureitisbasedonmodernizing
and integrating of some health information technology systems for
example: computerized physician order entry (COPE), Triage, clinical
decision support system (CDSS) types (diagnosis decision support
systems DDSS, case-based reasoning CBR, Knowledge-based and Non-
knowledge-based) and other new health information technologies
[computer therapy monitoring and computer drug dosing control based
on correlation between the drug pharmacokinetic (drug concentration)
and pharmacodynamics (patient improvement markers example fever,
heart rate, poising concentration) besides prevent drug toxicity and
potential doses related side effects], automatic therapy monitoring,
evaluating and remodifying. All linked together.
The Flow Chart
The diagnosis instructions and personalization of therapy will be
analysed from the patient data, which includes Age, Gender, maternal
state, chief compliant, symptoms, vital sings, past medical and
medications history (PMH) and ethnicity (biopsychosocial model).
After entering of the patient data the system/software will determining
the priority of patients (triage) and giving instructions for next step
either diagnosis for simple medicals conditions or diagnostics tests
that should done. The laboratories centres/departments will being
connected with the system/software in order to receive the diagnostics
values/results/findings, afterward the system/software will compare the
findings with the normal (analyse) to give the report of the result and
the final diagnosis what is more complications if it founded (Figures 1
and 2).
Then this followed by the recommendations by the system/
software for the best management option for the patient (whether
it is non-pharmacological or pharmacological/ options). Non-
pharmacological options for example: lifestyle modification, surgery,
cognitive behaviour therapy (CBT). For the pharmacological option the
personalize pharmacotherapy will be by the analysis/compare by the
system/software logic of patient data, diagnosis, laboratories findings
with the guidelines and recommendations that stored in the system.
The optimize therapy it is based on guidelines and recommendations,
age ,ethnicity, comorbidity, contraindications, cost), rights doses
[based on body weight ,age ethnicity (population-pharmacokinetic
like: pharmacogenetic, conditions alter pharmacokinetic, time
depending killing, concentration depending killing], the best route
of administration (based on pharmacokinetic-pharmacodynamics
approach, the ideal administration time (based on chronotherapy),
prevent drugs interactions (drug-drug, drugs-diseases, drugs-food,
drugs-recreational substances interactions), patient personalize
advice and counselling. Then evaluation and monitoring of therapy
(for examples evaluation of response and improvement, monitoring
parameters, computer therapy monitoring and computer drug dosing
control in certain cases for example: some chemotherapy agents, time/
concentrationdependkillingdrugsinhighresistanceorsuperinfections,
renal/hepatic impair patient) and follow-up, by the system whether
during hospitalizing (the system connected with the medical devices)
or in the follow up with simultaneously automatic regenerating of
protocols, guidelines, reconditions, adverse drug reactions reporting
and studies and researches supplying. The system will ensure covering
of the six criteria that measure and describe quality of care in health.
Demonstrate Example (Figure 3 and Table 1)
Advantages
Prevention of medical errors; reduce the work load and time
pressure. Minimize/manipulate the side effects and enhance treatment
outcome (Minimum treatment risks safe care). Efficacy and accuracy
of the decisions. Data retrieving and regeneration (self-develop
system/auto-updating, the data will be easily extracted for adverse
drugs reactions monitoring authorities and for studies and research).
Will possibility for covering all guidelines and recommendations,
combined together or based on selection separately. The system
covers and supports all aspects of clinical tasks triage, diagnosis,
treatment plan/decision, personal therapy, therapy evaluation and
monitoring. Considerations of chronotherapy, pharmacoeconomics,
pharmacokinetics, pharmacodynamics approaches and computer
therapy monitoring and computer drug dosing control.
Limitations
System development will have required time, so it will be better at
firsttodesignthreedifferentsystems:triage,diagnosissystem,Treatment
decision system, monitoring and evaluation system, then combine all as
one system. Cases that are more complex will require more time to be
recognized by the system. Healthcare professionals’ adherence to the
system will be required time. There are many guidelines (American,
British, European etc...), but it can be combined and integrated or
separated in the system as an option and preselection by healthcare
professionals to use combined or used particular guidelines.
Figure 1: The diagnosis instructions and personalization of therapy will be
analysed from the patient data, which includes Age, Gender, maternal state,
chief compliant, symptoms, vital sings, past medical and medications history
(PMH) and ethnicity (biopsychosocial model).
Figure 2: Recommendations by the system/software for the best management
option for the patient (whether it is non-pharmacological or pharmacological/
options).
3. Citation: Osman MAA (2018) A Prospective Medical System of the Future/a Complete Health Care System. J Gen Pract 6: 371.
Page 3 of 4
Volume 6 • Issue 4 • 1000371
J Gen Pract, an open access journal
ISSN: 2329-9126
Figure 3: The system will ensure covering of the six criteria that measure and describe quality of care in health.
The Current Medical Procedure A Prospective Medical System of the Future (PMSF)
Workload and Time Pressure
High workload and time pressure is common
[3,4,19,20].
Saving time it is known one of advantages of CDSS (clinical
decision support system) [7,21] although it cover one or two
clinical tasks. That is mean a PMSF will lead to significant
decrease in workload and time pressure.
Medical Errors
One of the most healthcare challenges
(Causatives vary from misdiagnosis, inaccurate
treatment or from physician, pharmacist, nurse)
[5,6,22,23].
This has proven by reduction in medical errors due to use of
CDSS (clinical decision support system) or computerized provider
order entry (CPOE) systems [8,9,24,25].
Efficacy and Safety
Likely to be poor since it is influence by
medical errors.
PMSF will improve efficacy and safety by selection of appropriate
diagnosis and treatment, prevention of medical errors, evaluation
and monitoring of therapy.
Data Retrieving Regeneration of guidelines and
recommendations.
Data retrieving for non-electronic health record
it is time consuming, uneconomical, lack of
accuracy.
Regeneration in majority it is manually.
Will save time and money, high accuracy this in term of data
retrieving.
Regeneration will be automatically.
Considerations of biopsychosocial model,
chronotherapy, pharmacoeconomics,
pharmacokinetics, pharmacodynamics approaches
and computer therapy monitoring and computer drug
dosing control.
It is rare apply of this approaches in current
medical procedure; due to training healthcare
professionals in this approaches. Besides of
complexity of the approaches.
PMSF will programmed with this approaches without need of
healthcare professionals knowledge about them and computer is
easily can solve complexity.
Adverse drug reaction reporting Procedure
The current ADRs reporting procedure depend
on paper and electronic entering of the data
and this result in discouraging of reporting.
According to a new study lack of time is one of
the barriers of ADRs reporting.
Whereas ADRs reporting procedure is proposed to be
spontaneously/automatically.
Data Collection
In most cases, data collect and then (may
decoded) enter into the computer in order to
analysis.
The Data/specific require data will extract from the system (and
decode automatically if it required) and analysis, without needing
of collection and entering.
Table 1: Differences between current medical procedure and a prospective medical system of the future.
Conclusion
A prospective medical system of the future will be inevitable for
introduction of the modernization of the health care processes; the best
treatment outcome (benefits). Moreover, minimum risks apart from
its practically user friendly (healthcare professionals); saving effort
and money; accelerate the health care process; ADRS reporting, make
4. Citation: Osman MAA (2018) A Prospective Medical System of the Future/a Complete Health Care System. J Gen Pract 6: 371.
Page 4 of 4
Volume 6 • Issue 4 • 1000371
J Gen Pract, an open access journal
ISSN: 2329-9126
studies and research easier and faster what is more in a modern method.
Acknowledgment
I would like to express my special thanks of gratitude to my GOD, my mother
Seham Mahdy Elasha; my father Ali Ahmad Osman; Mrs. Athensia Sevastaki
Belfast Met; JKKN College of Pharmacy Komarapalayam, Tamil Nadu, India; JSS
College of Pharmacy, Ooty, Tamil Nadu, India and finally The Government of the
United Kingdom.
References
1. The impact of computers in our daily lives computer science essay. Ukessays.
com 2016.
2. Artificial Intelligence in Medicine 42. Dl.acm.org. 2018.
3. Alonso CP, Martínez GL, Carrasco J, Solà I, Qureshi S (2011). The updating
of clinical practice guidelines: insights from an international survey. Implement
Sci 6.
4. Fricker L (2016) Drug discovery over the past thirty years: Why aren’t there
more new drugs. Einstein J Biol Medi, 29: 61.
5. https://epilepsychicago.org/epilepsy/treatment/factors-influencing-drug-
selection/
6. Proulx J (2014) Risk factors of adverse drug reaction–related hospitalizations
among seniors, 2006 To 2011. Value in Health 17: 16.
7. Misdiagnosis (1953) The Lancet 261: 1034.
8. Michon K, Attorney (2018) Medical malpractice: Misdiagnosis and delayed
diagnosis.
9. Slight SP, Howard R, Ghaleb M, Barber N, Franklin BD, et al. (2013) The
causes of prescribing errors in English general practices: A qualitative study. Br
J Gen Pract 63: e713-720.
10. Dean B, Schachter M, Vincent C, Barber N (2002) Causes of prescribing errors
in hospital inpatients: A prospective study. The Lancet 359: 1373-1378.
11. Bates DW, Boyle DL, Vander Vliet MB, Schneider J, Leape L (1995) Relationship
between medication errors and adverse drug events. J Gen Intern Med 10:
199-205.
12. Gill P, Stewart K, Treasure E, Chadwick B (2008) Methods of data collection in
qualitative research: Interviews and focus groups. BDJ 204: 291.
13. Schobel J, Pryss R, Schickler M, Reichert M (2016) June. Towards flexible
mobile data collection in healthcare. In Computer-Based Medical Systems
(CBMS) 2016.
14. Engel GL (1977) The need for a new medical model: A challenge for
biomedicine. Science 196: 129-136.
15. Engel GL (1980) The clinical application of the biopsychosocial model. Am J
Psychiatry 137: 535-544.
16. Frankel RM, Quill TE, McDaniel SH (2003) The Biopsychosocial Approach:
Past, Present,Future.University of Rochester Press, Rochester, NY.
17. Ransom ER (2008). The healthcare quality book: vision, strategy, and tools.
Chicago: Health Administration Press.
18. Chassin MR, Galvin RW (1998). The urgent need to improve health care quality:
Institute of Medicine National Roundtable on Health Care Quality. Jama, 280:
1000-1005.
19. McVicar A (2003) Workplace stress in nursing: A literature review. J Adv Nurs
44: 633-642.
20. MarineA, Ruotsalainen JH, Serra C, Verbeek JH (2006) Preventing occupational
stress in healthcare workers. Cochrane Database of Syst Rev 4 : CD002892.
21. Rodziewicz TL, Hipskind JE (2018) Medical error prevention. In StatPearls
[Internet]. StatPearls Publishing.
22. Cheragi MA, Manoocheri H, Mohammadnejad E, Ehsani SR (2013) Types and
causes of medication errors from nurse’s viewpoint. Iran J Nurs Midwifery Res
18: 228.
23. Murphy EV (2014) Clinical decision support: Effectiveness in improving quality
processes and clinical outcomes and factors that may influence success. Yale
J Biol Med 87: 187.
24. Jia P, Zhang L, Chen J, Zhao P, Zhang M (2016) The effects of clinical decision
support systems on medication safety: An overview. PloS one 11: p.e0167683.
25. Radley DC, Wasserman MR, Olsho LE, Shoemaker SJ, Spranca MD, et
al. (2013) Reduction in medication errors in hospitals due to adoption of
computerized provider order entry systems. J Am Med Inform Assoc 20: 470-
476.