Data quality assessment in health care: a 365-day chart
review of inpatients’ health records at a Nigerian
tertiary hospital
Adeleke IT*a, Adekanye AOb, Onawola KAb, Okuku AGc, Adefemi SAd, Erinle SAe,
Shehu AAa, Yahaya OEa, Adebisi AAa, AbdulGhaney OOa, Ogundiran LMa, Jibril ADf,
Atakere MEf Achinbee Ma, Abodunrin OAa, Wasagi MHa
a
Department of Health Information, b Department of Surgery, c Department of
Haematology & Blood Transfusion,d Department of Family Medicine, e Department of
Radiology, f Department of Obstetrics and Gynaecology, gFederal Medical Centre, Bida
Key words: clinical documentation, data quality, discharge summary, quality assurance
patients’ health records,
*Correspondence:
Adeleke, Ibrahim Taiwo
Department of Health Information Federal Medical Centre Bida
Preferred Postal Address: c/o Abu Mu’adh P.O. 207, Bida Niger State
E-mail: ibratadeleke_aliseyin@yahoo.com
Mobile Number: +234 8034229146 +234 8056503465
1
ABSTRACT
Background:
Health records is essential to good health care and its quality depends on information
entered by health professionals authorized to provide care and responsible for
documenting that care. Regular analysis of documents in the health records should be
performed to manage its content so it fulfills its purposes. This study assessed the
quantitative and qualitative properties of inpatients’ health records at Federal Medical
Centre, Bida in 2009.
Methods:
Health Records of 780 (9.2% of 2008 hospital discharges) patients amongst those who
were admitted and discharged between January 1st and December 31st 2009 were
randomly selected and reviewed to assess documentation quality of healthcare providers.
A 23-point health records review form by the World Health Organization as modified by
the authors was used as the review tool
Results:
A total of 733 (94.0% of selected records) folders were reviewed which comprised of
maternal related cases (336, 45.8%), paediatrics records (178, 24.3%), NHIS records (22,
3.0%) and 197 (26.9%) other specialties. The sample size represented the discharge
patterns of the hospital in the review year.
Documentation performance ratings was highest at 98.5% for promptness in documenting
care within the first 24 hours of admission, was fair at 58.8% for entry of unit numbers on
every page of case notes and very poor at 13.0% for writing discharge summaries.
In all, surgery cases were mostly (100%) documented within 24 hours, maternity cases
were more consistent (80.7%) in patient’s naming of case notes and ordering (91.1%) for
laboratory investigations while medical cases were more coherent (68.4%) in unit
numbering of case notes and perfect (94.3%) documentation of discharge summaries.
Four hundred and fifty five (62.1%) folders were chronologically arranged, 452 (62.3%)
folders tagged and a modest majority (591, 80.6%) of the folders were analyzed and
coded.
Conclusions:
No major data quality problems was revealed with the exception of irregular entries of
unit numbers and poor culture of completing discharge summary forms which may pose
threats to effective clinical coding and in effect, administrative data. However, when
discharge summaries were written, they were perfectly documented.
To further improve data quality in the hospital, establishment of a statutory Clinical
Documentation Improvement Program (CDIP) will be necessary for periodical review,
existing routine review needed to be strengthened and documentation processes
monitored by senior officers. Major limitations in the study include small records sample
size and non-case focused nature of the study. A more robust and case management
focused data quality improvement studies would be worthwhile in future
2
INTRODUCTION
Health record is a compilation of pertinent facts of a patient’s life and health history,
including past and present illness (es) and treatment (s), written by the health
professionals contributing to the patient’s care. It must be compiled in a timely manner
and contain sufficient data to identify the patient, support the diagnosis or reasons for
health care encounter, justify the treatment and accurately document the results. [1] It is
the visible evidence of what the hospital is accomplishing. The health record for each
patient is a compendium of critical material about the patient’s medical condition and the
actions taken to treat the individual. Therefore, it is strongly required that accurate and
timely documentation be provided for each patient on each contact with a health care
provider. [2]
Since it is one of the foundation blocks of any health institution, it must be
comprehensive and effective to bear the scrutiny of auditors or attorneys. [2] It has often
been said that an adequate health record indicates adequate care, and conversely, a poor
health record indicates poor care. It may be possible for a complete and thorough record
to exist for a patient who received poor care, but the reverse is more likely to be true. A
patient may have received adequate care, which is poorly documented. [1] The quality of
health records depends on information entered by health care professionals authorized to
provide care and responsible for documenting that care. The term ‘data quality’ refers to
characteristics and attributes of the data, specifically: accuracy, accessibility,
comprehensiveness, consistency, currency, definition, granularity, precision, relevancy,
and timeliness. [3] Problems with data quality make the health record linkage process
cumbersome, unreliable, and of little value to organisations, providers, and patients [4]
The information contained in the record is essential, but the process of documenting is
often a secondary priority of health caregivers. A busy physician may inadvertently
record a progress note in the wrong patient’s health records. A nurse may get a call to
assist a patient and forget to record medication given to another patient. [1] The value of
health records chart is ultimately dependent on their completeness and accuracy in
depicting patient information, which may vary with physician-patient communication,
patient’s condition and physician’s documentation styles. It is also true that coder
variability in coding diagnoses and procedures may be related to the inadequacy of
coder’s training and experience however, physician’s documentation may also impede
accurate interpretation of medical charts by coders and consequently the validity of
administrative data [5]. In Nigeria, dictation and manual systems are still dominant
feature in health records production since electronic health records or radical health
information systems has not been widely adopted [6] and where adopted, has not been
radically implemented. When dictation or manual system is used, the process of
producing accurate health records depends on the providers’ record keeping practices [7].
It has been established that patient’s health records review has improved how well
medical house officers document care in charts [8], has impact on the quality of health
record documentation [9] and enhances communication among health care team which
had resulted in improvement in patient’s care. [10] Health records review offers an
attractive mechanism for evaluating clinical competence because of its ease of
implementation relative to other methods. [8]
3
The World Health Organization (WHO) specifies leadership responsibility for
documentation and data quality as follows: health care administrators/managers, senior
doctors who are to ensure junior doctors record clinical data accurately and in a timely
manner, senior staff in departments such as laboratory, radiology, community nursing,
physical and occupational therapies, and social works. Most importantly, senior members
of health information departments are to ensure adequate documentation analysis,
assemblage and assessment in order to guarantee health record’s completeness,
availability and accessibility. [11] Therefore, health care institutions must facilitate a
process of qualitative and quantitative assessment of patients’ health records in order to
ensure that they meet purposes to which they are initiated.
Improving the quality of healthcare data in the patients’ health records can impact
management’s decision making in many ways [12] including; impact on health
economics, patient safety whilst undergoing care, evidence to support clinical decision
making through healthcare research, information provided to patients on their illness and
care and effectiveness of clinical care pathways
Regular analysis of the document in the health record should be performed in order to
manage the content of the health record so it fulfills its purposes of good patient care
information systems. The responsibility of ensuring such a quality patient care
information creates a challenge to the health record systems in a complex and ever
changing health care delivery system. [1]
Regular chart reviews are ongoing in the Department of Health Information albeit, the
quest to put research-based professional-cum-scientific approaches for data quality
improvement propelled this study.
4
METHODS:
Objective of the Study:
The objective of the study is to assess the quantitative and qualitative properties of
inpatients’ health records at Federal Medical Centre, Bida Niger State Nigeria.
Setting:
The study was carried out using selected discharged folders of patients from all the 13
wards of Federal Medical Centre, Bida which were grouped according to specialties for
data clarity and brevity without any loss of linkage to source. These specialties include:
medicine, surgery, obstetrics & gynaecology, paediatrics, psychiatry and accident &
emergency. All wards were represented in data abstraction even with the sampling
methods employed.
Study Materials:
Health Records of patients who were admitted and discharged between January 1st and
December 31st 2009 were reviewed.
A health records review form by the World Health Organization [11] as modified by the
authors was used as the review tool. It has 23-point review questions which evaluate
some qualitative and quantitative properties of administrative and clinical documentation
in the patients’ health records. Items evaluated include: promptness of documentation by
contributors, clinical quality and detailing, laboratory investigation orders, discharge
decisions and follow up plans and finally, issues of assembly and discharge analysis and
clinical coding. A five-point score rating was used to measure documentation
performance. Cases with 90% and above rate are regarded as excellent performance, 7089% as very good, 60-69% as good, 40-59% as fair and below 40% as poor
documentation performance. Reviewers attach yes or no ratings to each of the questions.
Review Sessions:
The review took place between November 2009 and June 2011. Nine of us AIT, SAA,
YOE, AAA, AOO, OLM, AM, AOA and WMH participated in the weekly generation of
discharged case lists directly from the hospital daily ward statement forms in the
Department of Health Information. We also participated in systematic random sampling
for selection, case retrieval and filing of selected records. We monitored and participated
in chart review and data abstraction. AIT entered abstracted data onto SPSS. All the
reviewers are health information management professionals who have at one time or the
other worked in the Clinical Coding Unit of the Department. There, routine charts review
are done for assembly and discharge analysis and subsequent coding and indexing which
must have added value to reviewers’ formal training. The review team had weekly
meetings to select 15 discharged patients’ health records each due to the time-consuming
nature of the review exercise and to avoid boredom in review and abstraction. This gave a
total sampling size of 780 records which amounted to 9.2% of the average annual discharges
in the hospital using year 2008 Annual Statistics. [13] AIT, SAA, and YOE peer-reviewed
the works of the seven other reviewers and at every point called their attention to omitted or
committed errors. The sessions continued uninterruptedly for the first eight months. Since
the beginning of the 9th month, there was reduction in the number of records reviewed per
week. In all, the review was done for all the discharged records in the 52 weeks in the year
2009 but, the review sessions extended to 85 weeks (November 2009 to June 2011).
5
Statistical Analysis:
The statistical software SPSS 17.0 by SPSS Inc an IBM Company (2007) was used for
data analysis. Analysis done include: simple frequency table, cross tabulation, bar chart,
mean and standard deviation
Ethical Consideration:
The ethical approval to conduct this research was granted by the Research Ethics and
Review Committee of Federal Medical Centre, Bida. It is noteworthy here to declare that
it was difficult to obtain consent from individual patients who were the subjects of the
records. Although, ethical requirements demand that patients should give explicit consent
for their records to be used in research, local research ethics committees have the
discretion to approve such research and journals to publish the findings when access to
the health record is essential for the completion of the research and consent is not
practicable. [14]
However, all direct identifiers of patients were removed before data abstraction and
subsequent transfer of information onto the computer system. As such, all records were
anonymized. The only possible but difficult identifier for information hackers to access
left in the abstraction was unit number which was used to track duplication and monitor
abstraction processes. It is difficult because, only trained and licensed health records
officers participated in the abstraction and non-professionals and unauthorized users
cannot gain access to the health records library where such numbers are used to trace
patients’ health records. In addition, the patient’s unit number is nowhere displayed in the
study and its final analysis. As such, all patients’ health records were de-identified before,
during and beyond the study. When information is de-identified, all personal
characteristics have been stripped so it cannot be later constituted or combined to reidentify the individual. [15]
6
RESULTS
Demographic patterns of abstracted records
Seven hundred and thirty-three folders were abstracted out of 780 folders projected,
giving an overall analyzable rate of 94.0%. The 47 folders not abstracted were actually
selected as they satisfied abstraction criteria but, were not found on shelves as at the time
of abstraction. Further search efforts revealed that some patients who were the subjects of
the folders were on admissions and that some folders were out of shelves for
administrative and evaluation purposes. Twenty-one (2.9%) of the folders experienced
more than one hospitalizations in the period under review meeting eligibility criteria
twice or more. This might also be due to transfer across wards (e.g. from A&E to Male
Surgical wards). Good to note is that 22 (3.0%) of the folders were folders of patients
under National Health Insurance Scheme (NHIS).
Abstracted records across wards (in specialties) were; Obstetrics & Gynaecology (336,
45.8%), Paediatrics (178, 24.3%), Accident & Emergency (140, 19.1%), Medicine (38,
5.2%), Surgery (30, 4.1%), Psychiatry (10, 1.4%) and Amenity (1, 0.1%).
This virtually represents the actual situation in the hospital as evident from the hospital
discharges in the year under review (2009). Obstetrics & Gynaecology recorded 38.6% of
total discharges, while Paediatrics recorded 23.1%, Accident & Emergency 23.7%,
Medicine 6.5%, Surgery 6.4%, Psychiatry 1.0% and Amenity 0.7% respectively. [16]
The abstraction covered all the 52 weeks in year 2009 as cases discharged in all these
weeks were represented in the abstracted data with week 7 having the highest (27, 3.7%)
and week 28, the lowest (4, 0.5%). Similarly, majority (436, 59.5%) of the subjects of the
folders abstracted spent between one to five days on admission while only 2 (0.3%) had
their stay above 90 days. The mean length of stay and standard deviation were 2.63 and
1.8 respectively
Table 1: Data Reliability, Consistency and Responsibility for care
Frequency
Documentation Standards
fulfilled
(%)
Records documented by clinicians within
the first 24hrs of admission
722 98.5
Nursing care plans documented within the
first 24 hrs of admission
628 85.7
Patient's name properly documented in
first page of all continuation sheets
551 75.2
Unit number recorded in first page of all
continuation sheets
431 58.8
Progress notes documented daily
673 91.8
Progress notes duly signed with dates daily
646 88.1
Investigation order forms duly signed with
dates
529 72.2
7
Table 1 above shows issues of responsibility for care such as proper documentation with
signatures within the first 24hours by nurses and doctors. As much as issues of
consistency in documentation; like continuous naming of progress notes, and daily
documentation of events of care. Degree of documentation performance ranges from
highest 98.5% for documentation by clinicians in the first 24 hours of admission to 58.8%
for documentation of unit number at every page of the case notes. The overall
performance as observed was encouraging.
Table 2: Clinical Detailing and Quality Assurance
Documentation Standards
Frequency fulfilled
(%)
Records contains past medical history
Provisional diagnosis documented in
the records
643 87.7
Investigation order forms duly filled
617 84.2
Discharge notes recorded
Discharge summary completed
Discharge summary contains summary
of hospitalization
Discharge summary contains treatment
and medication administered
641 87.4
95 13.0
691 94.3
91 95.5
90 94.7
Discharge summary contains follow up
details
89 93.7
From Table 2 above, important clinical processes such as history of patient’s past medical
history, working or provisional diagnosis, proper order of investigation forms and
relevant notes on discharge were properly documented in most of the abstracted records
(87.7%, 94.3%, 84.2% and 87.4% respectively). However, discharge summary was not
completed for the highest majority of the abstracted inpatients’ records as only a few (95,
13.0%) of all abstracted folders contained discharge summary. In spite of this, it is
evident that the few records with discharge summary were correctly and properly
documented. For revelation, all the three subsections of the discharge summary notes
analyzed were excellently performed: summary of hospitalization (95.5%), treatment and
medication (94.7%) and follow up details (93.7%).
8
Table 3: Essential Documentation by Specialties/subspecialties
Ward
A&E
Amenity
Medicine
Obst &
Gynae
Paediatrics
Psychiatry
Surgery
%
Documented
within 24hrs
97.9
100
97.4
% Patient's
name
properly
documented
75.7
0
68.4
99.1
98.3
90
100
80.7
65.7
80
76.7
% Unit
number
%
duly
Discharge
documented Summary
58.6
7.1
0
100
68.4
18.4
61.9
49.4
60
70
% Principal
diagnosis
documented
in discharge
summary
90
100
100
%
Investigation
Orders
properly
documented
71.4
100
84.2
94.3
88.2
0
85.7
91.1
83.1
40
86.7
15.8
9.6
0
23.3
Subspecialty distribution of quality documentation is depicted in Table 3 above and
Figure 1 below. Folders on surgical cases were mostly documented (30, 100%) within 24
hours while psychiatric cases faired the least (9, 90%). Obstetrics and Gynecological
cases were more consistent (271, 80.7%) in patients’ naming at every page while surgical
cases were most coherence (21, 70%) in identifying patients’ unit number at every page.
Adequate completion of discharge summary was not given priority at all, as surgical
cases which recorded the highest led poorly with 23.3% and there was no discharge
summary at all amongst psychiatric cases. However, those few cases that contained
discharge summary were wholesomely completed (e.g. principal diagnoses recorded in
all medical cases with discharge summary). Obstetrics & Gynaecology department
ordered laboratory investigations more often (306, 91.1%) and endorsed with date (259,
77.1%) than any other department, while psychiatric department ordered least (4, 40%)
for laboratory investigations.
Figure 1: % Completed Discharge Summary across Specialties
23.3
Surgery
0
Psychiatry
9.6
Paediatrics
15.8
Obst & Gynae
18.4
Medicine
7.1
A&E
0
5
10
15
% Compliance
9
20
25
Table 4: Discharge, Assembly Analysis and Clinical Coding
Documentation Standards
Frequency fulfilled
Case notes chronologically arranged
in the folder
Case folders tagged
Discharge analysis and clinical
coding done
(%)
455 62.1
457 62.3
591 80.6
Although most patients’ health records were somewhat chronologically arranged (455,
62.1%) and tagged (457, 62.3%), majority of them were analyzed and properly coded
(591, 80.6%) before being subsequently return to the filing areas
10
DISCUSSION
Some patients’ health records could not be found for data abstraction due to posttreatment patients’ requests which must be obliged and inpatient re-admissions during the
review period. The rate of unseen records was low (6.0%) and did not in any way
compromise the validity of the study. The records sample size projected for the entire
hospital (780, 9.2% of 2008 Annual Discharges) and the actual records reviewed (733,
7.9%) was too small to generalize the actual hospital documentation performance and to
ensure quality. This makes our study consistent with the limitation reported by Baker JG
et al [17] that 12% record sample size was not large enough to ensure quality for the
entire studied mental division. Our study cut across all services including all specialties
and subspecialties in the hospital. It is an improvement on the study by O’Neil et al [18]
where only one service was examined. In our study, records of maternal related
healthcare constituted almost half (336, 45.8%) of the total reviewed records, children
almost one quarter (178, 24.3%) and records of those who participate in the ongoing
Federal Government healthcare initiative –National Health Insurance Schemes were 22
(3.0%) of the whole sample.
Generally, our findings depict consistency and promptness in documenting care, featuring
past medical history and making provisional diagnoses across specialties. This is
consistent with the findings by Durkin et al [2] where there are more consistent findings
in the various categories of documentation requirements. It is contrary to the findings by
Gunninberg et al [19] where the quality of documentation in patient record is generally
poor. However, our study revealed that laboratory investigation orders were not
consistently signed when ordered, writing of patients’ unit number was somewhat done
and discharge summaries were largely not documented. Non-signing of investigation
forms ordered might mean not taking full responsibility of care and the somewhat entry
of unit number tend to lead to loss of personal records of individual patient. The unit
number is even the unique identifier that distinguishes one patient from the other
especially in the study setting where more than one members of a family share the same
names. Non-completion of discharge summary is contrary to standards as identified by
Kind & Smith [20]. Irregular entry of unit numbers and non completion of discharge
summaries which signify incompleteness of medical charts may constitute majorly to
suboptimal valid administrative data for management’s decisions making [5]
It has been established from our findings that surgeons were more prompt in
documentation and were better than others in the usage of discharge summaries.
Physicians were better in the completion of discharge summaries and gynaecologists
mostly ordered laboratory investigations. However, we discovered that paediatricians
faired averagely in all aspects and psychiatrists don’t write discharge summaries and they
ordered less laboratory investigations
11
Our study discovered that case notes content of patients’ health records were not always
chronologically arranged and tagged. Reasons for the less tagged notes might be due to
recurrent out-of-stock of file tags in the hospital’s central stores. The Clinical Coding
Unit of the hospital where assembly and discharge analysis are done requires to be
humanly strengthened in order to effect proper arrangement of case notes. The fact that
clinical coding process is ongoing tended to show a level of improvement in health
information management as that of a tertiary hospital in Nigeria. By standard, every
discharged case deserves coding analysis as it plays an important role in determining
reimbursement for healthcare facilities [21] and improves clinical research. Clinical
coding processes in the hospital could better be done if all discharged cases were
analyzed and coded.
In the overall, our findings demonstrate that documentation standards were well adhered
to except in the entry of unit numbers across notes and the completion of discharge
summaries though, the completed few were close to perfection. However, given the
discharge summary’s pivotal communication role in healthcare transitions, even a small
frequency of omitted patient discharge condition information is a concern and may
influence patient safety. [20] How much more of the existing gaps in the discharge
summaries in our findings. Also, non-usage of the discharge summaries has rendered
useless; the huge investment by the hospital authority in the production of discharge
summary forms in dozens of thousands. This may mean non-compliance to theories of
economics and financial management.
Our findings revealed that regular hospital-wide comprehensive assessments of both
qualitative and quantitative properties of patients’ health records in terms of Clinical
Documentation Improvement Program (CDIP) [22] will further reveal other areas of
inconsistencies with documentation standards as specified by Kind & Smith [20]. In
addition, the authorities need to support financially, the technical capacities of the
reviewer team as it is capital intensive. Financial constraints might impede [9] such
regular and comprehensive hospital-wide reviews. This program will improve how well
clinicians and other contributors document care in the charts [8] [23], impact positively
on the quality of patients’ health records [9], enhance communication amongst health
care teams, improve patients’ care [10] [22], leads to higher reimbursement [22] and
evaluate the competence of clinicians and clinical support professionals and other
hospital staff [7]
Healthcare providers in this hospital are dedicated to quality documentation of care in the
patients’ health records. However, there is observed indifference in their entry of
patient’s unit number and in completion of discharge summary forms which is an
essential component of the patient’s health records and a pointer to what decision the
administration takes.
12
CONCLUSION & RECOMMENDATIONS
Our study did not reveal any major data quality problems per se with the exception of
irregular entry of unit numbers and poor culture of completing discharge summary forms
which may pose threats to effective clinical coding and in effect, administrative data.
However, when discharge summaries were written, they were perfectly documented.
A statutory Clinical Documentation Improvement Program (CDIP) will be necessary for
holistic periodical review of chart documentation in the patients’ health records to
improve data quality in the hospital. In the same vein, the routine chart review in Clinical
Coding & Indexing Unit of the Department of Health Information would need to be
strengthened.
More so, all contributors to the medical chart in the patients’ health records would require
orientation and re-orientation in health data quality through the existing continuing
education programs of the hospital and mentorship role of their senior colleagues. Major
limitations in our study include the not too large sample size and that the study only
focused quantitative and data qualitative properties of the patients’ health records with
the exception of target indices like case management. A more robust and case
management-focused data quality improvement studies will be worthwhile in future
13
Authors’ Contributions:
All of the authors read and approved the final manuscript.
1. AIT conceived of the study, initiated the design, handled data entry and
participated in recruitment of records, chart review, data abstraction, coordination,
analyzed the data and drafted the manuscript
2. AAO participated in study design, data analysis, coordination and revised the
manuscript
3. OKA participated in study design, data analysis coordination and revised the
manuscript
4. OAG participated in study design, data analysis coordination and revised the
manuscript
5. ASA participated in study design, data analysis coordination and revised the
manuscript
6. ESA participated in study design, data analysis, coordination and revised the
manuscript
7. SAA participated in study design, selection of records, records review, data
abstraction, and revised the manuscript
8. YOE participated in study design, recruitment of records, records review, data
abstraction, and revised the manuscript
9. AAA participated in study design, selection of records, data abstraction, and
revised the manuscript
10. AOO participated in study design, selection of records, data abstraction, and
revised the manuscript
11. OLM participated in study design, recruitment of records, data abstraction, and
revised the manuscript
12. JAD participated in study design, data analysis, coordination and revised the
manuscript
13. AME participated in study design, data analysis, coordination and revised the
manuscript
14. AM participated in study design, recruitment of records, records review, data
abstraction, and revised the manuscript
15. AOA participated in study design, recruitment of records, data abstraction, and
revised the manuscript
16. WMH participated in study design, recruitment of records, data abstraction, and
revised the manuscript
ACKNOWLEDGEMENT
The authors wish to thank NM Amari and DI Adeoti of the Department of Health
Information for their assistance towards the tail end of chart review and data abstraction.
14
Authors’ Affiliations:
1. AIT is a Principal Health Records Officer and Coordinator, Department of Health
Information Federal Medical Centre, Bida. He holds HND in Health Information
Management from UCH Ibadan
2. AAO is a Consultant Urologist and Head Department of Surgery Federal Medical
Center, Bida. He holds MBBS and a Fellow West African College of Surgeons
3. OKA is a Principal Medical Officer with bias in Urology with the Department of
Surgery Federal Medical Centre, Bida
4. OAG is a Consultant Haematologist and Medical Director/Chief Executive
Officer Federal Medical Centre, Bida. He holds MBBS, a member of NIM and a
Fellow, National College of Pathologists
5. ASA is a Consultant Family Physician with the Department of Family Medicine
Federal Medical Center, Bida. He holds MBBS, a fellow of both West African
College of Physicians and National College of General Practices
6. ESA is a Consultant Radiologist, Head Department of Radiology and the Head of
Clinical Services Federal Medical Centre, Bida. He holds MBBS and fellow of
both National Medical College of Radiologists and West African College of
Surgeons
7. SAA is a health information management professional with the Department of
Health Information Federal Medical Centre, Bida. Studies Health Information
Management at UDUTH Sokoto
8. YOE is a Health Records Officer with the Department of Health Information
Federal Medical Centre, Bida. He holds HND in Health Information Management
from UITH Ilorin
9. AAA is a health information management professional with the Department of
Health Information Federal Medical Centre, Bida. He holds HND in Health
Information Management from UITH Ilorin
10. AOO is a health information management professional with the Department of
Health Information Federal Medical Centre, Bida. He holds HND in Health
Information Management from UITH Ilorin
11. OLM is a health information management professional with the Department of
Health Information Federal Medical Centre, Bida. He holds HND in Health
Information Management from UITH Ilorin
12. JAD is a Registrar in training with the Department of Obstetrics & Gynaecology
Federal Medical Center, Bida. He holds MBBS
13. AME is a Registrar in training with the Department of Obstetrics & Gynaecology
Federal Medical Center, Bida. He holds MBBS
14. AM is a health information management professional with the Department of
Health Information Federal Medical Centre, Bida.
15. AOA is a health information management professional with the Department of
Health Information Federal Medical Centre, Bida. He holds HND in Health
Information Management from UITH Ilorin
16. WMH is a health information management professional with the Department of
Health Information Federal Medical Centre, Bida. He holds HND in Health
Information Management from UDUTH Sokoto
15
References
1. Huffman EK. Medical Record Management (Ninth Edition). Berwyn, Illinois:
Physician’s Records Company, 596-597 p.
2. Durking N. Using Records Review as a quality Improvement Process. Home Care
Nurse. 2006; 24(8): 492-502
3. Teslow MS, Wilde DJ. (2001). Data collection standards. In Health information:
management of a strategic resource. (2ndedition). M. Abdelhak (Ed.).Philadelphia,
W.B Saunders Company
4. Kerr E, McGlynn E, Damberg C. (1998). Developing and using a clinical
information system. In: Health information systems: design issues and analytic
applications. E. McGlynn et al. (Eds). Santa Monica, RAND Health.
5. So L, Beck CA, Brien S, et al. Chart Documentation Quality and its relationship
to the Validity of Administrative Data Discharge Records. Health Informatics
Journal. 2010; 16(2): 101-113
6. Adeleke IT. ICT knowledge, utilization and training needs amongst health
information management professionals in Nigeria.(2010) A completed study.
7. Cora A, David GC, Chand D. Understanding the work of Medical
Transcriptionists in the production of Medical Records. Health Informatics
Journal. 2010; 16(2): 87-100
8. Donald AO. The Impact of Feedback to Medical House Staff on Chart
Documentation and Quality of Care in the Outpatient Setting. Journal of General
Internal Medicine. 2002; 12(6): 352-356
9. Reisch LM, Fosse JS, Beverly K, et al Training, Quality Assurance and
Assessment of Medical record Abstraction in a Multi-site Study. American
Journal of Epidemiology. 2003; 157, 546-551
10. Oldfield M. Case Study: Changing Behaviours to improve Documentation and
optimize Hospital Revenue. Nursing Leadership, 2007; 20(1), 40-48
11. Abdelhak M. Improving Data quality: A Guide for Developing Countries. United
Nations Avenue, 1000 Manila, Philippines: Regional Office for the Western
Pacific, 18 p.
12. Kerr KA, Norris T, Stockdale R. The strategic management of data quality in
health care. Health Informatics Journal. 2008; 14(4): 259-266
13. 2008 Annual Report. Federal Medical Centre, Bida. 52pp
14. Doyal L. Informed consent in medical research: Journals should not publish
research to which patients have not given fully informed consent–with three
exceptions. BMJ 1997;314:1107 (12 April)
15. Rinehart-Thompson LA. Legal Issues in Health Information Management
Technology. In: Health Information Management Technology: An Applied
Approach. (Second Edition) Johns ML (Ed). American Health Information
Management Association. 2007: 704-705
16. 2009 Annual Report. Federal Medical Centre, Bida. 33pp
17. Baker JG, Shanfield SB, Schnee S. Using Quality Improvement Teams to
improve Documentation in Records at a Community Mental Health Centre.
Psychiatrtric Services, 2000; 51, 239-242
16
18. O’Neil A.C., Peterson L.A., Cook F., Bates D.W., Lee T.H., Brennan T.A.
Physician Reporting Compared with Medical Record Review to Identify Adverse
Medical Events. Annals of Internal Medicine. 1993; 119(5): 370-376
19. Gunningberg L., Ehrenberg A., Doughty D. Accuracy and Quality in the Nursing
Documentation of Pressure Ulcers: A Comparison of Record Content and Patient
Examination. The Journal of Wound, Ostomy and Continence Nursing. 2004;
31(6): 328-335
20. Kind AJH, Smith MA. Documentation of Mandated Discharge Summary
Components in Transitions from Acute to Sub-acute Care. In: Henriksen K,
Battles JB, Keyes MA, Grady ML, editors. Advances in Patient Safety: New
Directions and Alternative Approaches (Vol. 2: Culture and Redesign). Rockville
(MD): Agency for Healthcare Research and Quality (US); 2008 Aug.
21. Scott K. Clinical Vocabularies and Classification Systems. In: Health Information
Management Technology: An Applied Approach. (Second Edition) Johns ML
(Ed). American Health Information Management Association. 2007: 218pp
22. Dimick C. Documentation Improvement: Three successful HIM-led Programs.
Journal of American Health information Management Association. 2008; 79(7):
40-44
23. Thomas E.J., Stoddert D.M., Brennan T.A. The Reliability of Medical Record
Review for Estimating Adverse Event Rates. Annals of Internal Medicine. 2002;
136(11): 812-816
17