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Problems in the structure, consistency, and completeness of electronic health record data are barriers to outcomes research, quality improvement, and practice redesign. This nonexperimental retrospective study examines the utility of importing de-identified electronic health record data into an external system to identify patients with and at risk for essential hypertension. We find a statistically significant increase in cases based on combined use of diagnostic and free-text coding (mean = 1,256.1, 95% CI 1,232.3-1,279.7) compared to diagnostic coding alone (mean = 1,174.5, 95% CI 1,150.5-1,198.3). While it is not surprising that significantly more patients are identified when broadening search criteria, the implications are critical for quality of care, the movement toward the National Committee for Quality Assurance's Patient-Centered Medical Home program, and meaningful use of electronic health records. Further, we find a statistically significant increase in potential cases based on the last two or more blood pressure readings greater than or equal to 140/90 mm Hg (mean = 1,353.9, 95% CI 1,329.9-1,377.9).
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Identifying Patients with Hypertension: A Case for Auditing Electronic Health Record Data
Identifying Patients with Hypertension:
A Case for Auditing Electronic
Health Record Data
by Adam Baus, MA, MPH; Michael Hendryx, PhD; and Cecil Pollard, MA
Abstract
Problems in the structure, consistency, and completeness of electronic health record data are barriers
to outcomes research, quality improvement, and practice redesign. This nonexperimental retrospective
study examines the utility of importing de-identified electronic health record data into an external system
to identify patients with and at risk for essential hypertension. We find a statistically significant increase
in cases based on combined use of diagnostic and free-text coding (mean = 1,256.1, 95% CI 1,232.3–
1,279.7) compared to diagnostic coding alone (mean = 1,174.5, 95% CI 1,150.5–1,198.3). While it is not
surprising that significantly more patients are identified when broadening search criteria, the implications
are critical for quality of care, the movement toward the National Committee for Quality Assurance’s
Patient-Centered Medical Home program, and meaningful use of electronic health records. Further, we
find a statistically significant increase in potential cases based on the last two or more blood pressure
readings greater than or equal to 140/90 mm Hg (mean = 1,353.9, 95% CI 1,329.9–1,377.9).
Keywords: electronic health record, data quality, outcomes research, quality care, hypertension,
registry
Introduction
The benefits of electronic health records (EHRs) for primary care and the application of these systems
to outcomes research and current efforts in practice redesign such as the National Committee for Quality
Assurance’s Patient-Centered Medical Home program are often hampered by barriers to full integration
of EHRs. Common barriers include lack of trust in EHRs to securely store medical records,1–5 physicians’
views that EHRs interfere with clinical judgment;6, 7 lack of standards in data formatting and lack of
interoperability;8–16 the required time, training, and investment to become proficient in using the
systems;17–22 the absence of local leadership to champion the systems;23–27 difficulties in organizational
redesign to use the EHR;28–34 and lack of readiness to implement EHRs successfully.35–37 We sought to
examine one problem—the structure, consistency, and completeness of EHR data—by importing de-
identified EHR data into an external system for analysis of diagnostic information.
Background
EHRs have the potential to be valuable tools for health outcomes research in primary care38–43 and a
critical component in practice redesign and prevention of chronic diseases such as hypertension through
identification of at-risk patients.44, 45 While manual review of medical records is resource intensive,46
using diagnosis codes stored within EHRs permits searching in a more comprehensive and efficient
2 Perspectives in Health Information Management, Spring 2012
manner.47 However, problems in the structure, consistency, and completeness of EHR data and the use of
free-text entries rather than discrete data fields48–58 create barriers to research, outcomes reporting, and
quality improvement activities, particularly among smaller, rural practices.59–65
Given the challenges created by free-text data entry into EHRs, the current study examines the ability
to identify cases with essential hypertension by importing de-identified EHR data from 11 West Virginia
primary care centers into an external system, in this case a public-domain patient registry. An advantage
of the registry is that it is accessible at the practice level and requires no programming or statistical
expertise to use. This study examines whether patients with a diagnosis of essential hypertension are
missed if searching only by International Classification of Diseases, Ninth Revision, Clinical
Modification (ICD-9-CM) diagnostic codes (401.0–401.9). ICD-9-CM coding is currently used in this
particular EHR system. We test the hypothesis that there will be significantly fewer patients identified
with hypertension based on ICD-9-CM diagnosis codes relative to use of diagnosis codes plus free-text
coding of hypertension. Support of this hypothesis would document the benefits of auditing EHR data for
completeness and consistency, inform quality improvement efforts in overcoming barriers to EHR data
quality and reliability, and support the National Committee for Quality Assurance health information
framework, which highlights the need for interventions designed to improve the management and
application of EHR data for research and quality improvement. Improving the management and
application of EHR data has gained increased attention as a vital component in the overall success of
health information technology endeavors.66–76 Secondarily, we hypothesize that significantly more cases
will be identified through a third measure based on the guidelines for diagnosis of hypertension presented
in the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and
Treatment of High Blood Pressure (two or more most recent blood pressure readings greater than or equal
to 140/90 mm Hg among those without any diagnosis of essential hypertension).77 This third measure
identifies those at risk for or undiagnosed with hypertension, and would help document the benefits of
analyzing EHR data in an external format.
Methods
This research is a nonexperimental retrospective study of essential hypertension cases identified
across 11 West Virginia primary care centers using the same EHR system. We used a previously
developed tool to import data from the EHRs into the Chronic Disease Electronic Management System
(CDEMS).78 CDEMS is a Microsoft Accessbased public-domain registry originally developed by the
Washington State Department of Health. Moving the de-identified EHR data to an external system, in this
case a registry, allows for data transparency in that key data within the EHR (i.e., patient diagnoses,
demographics, vitals, laboratory results and services) can be queried for coding consistency and
completeness. Table 1 lists all data elements imported from the EHR into the registry. This registry was
chosen as the tool for analysis because it is accessible by each primary care center, allowing methods and
tools for this research to be applied at the practice level for quality improvement efforts in data
management, identification of at-risk patients, and quality-of-care improvement. De-identified data
included in this analysis are for all active patients in the 11 sites as of December 31, 2010.
Queries were built in the registry to search the de-identified EHR data to 1) identify unduplicated
patients with a diagnosis of essential hypertension based on ICD-9-CM codes (using the diagnosis and
demographic portions of the data); 2) identify unduplicated patients with a diagnosis of essential
hypertension based on free-text entries (using the diagnosis and demographic portions of the data); and 3)
identify unduplicated patients whose last two or more blood pressure readings were greater than or equal
to 140/90 mm Hg and who did not have a documented diagnosis of essential hypertension in either ICD-
9-CM code or free-text format (using the diagnosis, demographic, and vital signs portions of the data).
Table 2 provides a listing of queries used to identify patients, a description of the functions of each query,
and a list of the free-text diagnoses that were detected. Identification of patients with a diagnosis of
essential hypertension by ICD-9-CM code was accomplished by limiting search criteria to codes 401.0
401.9. Identification of patients with a free-text diagnosis of essential hypertension required more search
steps. Search of the diagnosis field called “Other” required use of the LIKE condition function in
Identifying Patients with Hypertension: A Case for Auditing Electronic Health Record Data
Microsoft Access, also known as wildcards, to locate all matching criteria. Wildcard expressions used in
the search were “*Hyperten*”; “*HTN*”; “*401*”; and “*Hyper ten*”. Upon review of all free-text
results, it was evident that the search returned results relating to other forms of hypertension that needed
to be excluded in this study. Excluded from search criteria were the following: “*Pulm*”; “*Neph*”;
“*Coat*”; “*Retino*”; “*Pre*”; “*Ocul*”; “*Occul*”; “*Portal*”; “*Gastro*”; “*Partum*”; “*Ortho*”;
“*Oval*”; “*Border*”; “*Myopathy*”; “*Medic*”; “*Renal*”; “*Venous*”; “*FH*”; “*Family*”;
“*Gest*”; “*Decea*”; “*Tight*”; “*Disea*”; “*Infar*”; “*Intracran*”; and “*Elevated*”. Means,
standard deviations, and 95 percent confidence intervals were calculated for the number of cases to
measure the differences between each method.
Results
Based on use of ICD-9-CM codes alone, 12,919 unduplicated patients with essential hypertension
were identified in the 11 sites. Searching free-text diagnoses, 898 additional unduplicated patients were
identified. Broadening the search criteria to patients whose last two or more blood pressure readings were
consistently greater than or equal to 140/90 mm Hg identifies an additional 1,076 unduplicated patients
not identified by ICD-9-CM codes or free-text entries (range = 297). Use of all three methods identified
14,893 cases. Table 3 presents these findings.
Placing confidence intervals around the means of each patient count method, we find a statistically
significant increase in total cases identified with essential hypertension based on combined use of ICD-9-
CM coding and free text (mean = 1,256.1, 95% CI = 1,232.3–1,279.7) compared to ICD-9-CM coding
alone (mean = 1,174.5, 95% CI = 1,150.5–1,198.3). Furthermore, we find a statistically significant
increase in identification of potential cases based on cases in which the last two or more blood pressure
readings were greater than or equal to 140/90 mm Hg (mean = 1,353.9, 95% CI = 1,329.9–1,377.9)
compared to ICD-9-CM coding plus free-text search. Use of only ICD-9-CM codes missed 13.3 percent
of cases as identified using all three methods. Table 4 and Figure 1 present these findings.
Discussion
By auditing EHR data in an external system, this study finds significant limitation in the ability to
identify patients with a diagnosis of essential hypertension due to the use of free-text diagnosis entries.
This study allows for the identification of a problem in data quality and completeness, and is translational
in nature in that the study methods and tools are accessible to each site to monitor documentation
processes and make adjustments as needed without time-intensive chart reviews or special programming.
Further, importing the EHR data into an external system allows for analysis of blood pressure results to
identify patients either undiagnosed with or at risk for development of the condition.
While it may not be surprising that significantly more patients are identified when broadening the
search criteria, the implications are critical for quality of care because the identification of patients by
health condition is a fundamental step in the process of applying data for quality improvement and
reporting. Furthermore, the ability to accurately report data at the population level is central to the Patient-
Centered Medical Home program and to meaningful use criteria. The inability to capture all patients by
health condition yields reports for Patient-Centered Medical Home and meaningful use purposes that are
inaccurate. Likewise, EHRs offer the promise of better patient care through decision support tools such as
those that suggest care guidelines and treatment based on a patient’s health condition. However, problems
in data quality can result in lower levels of provider trust in the data and the therefore decreased
application of EHR data to patient care. Auditing data within the EHR can help identify these problems
and provide the opportunity to correct them, for example, through training on the use of EHRs and
through development of practice policies and procedures aimed at eliminating free-text entry of
diagnoses. While it may not be feasible to alter the structure or functions of the EHR, it is reasonable to
expect that quality improvement efforts centered on training and practice policies will help overcome
barriers to data quality.
The substantial variability between clinics that was detected enables identification of clinics that
follow best practices (i.e., those with relatively low proportions of diagnoses of hypertension recorded by
4 Perspectives in Health Information Management, Spring 2012
free text, such as clinics E and F in Table 4), from which other sites can learn and apply documentation
practices and policies. Likewise, this analysis aids in identifying clinics at which data management
support and follow-up training is warranted (e.g., clinics J and G in Table 4). While clinic-level variability
is not addressed within this study, results from this research allow for follow-up research efforts to be
designed and conducted with these sites.
Conclusions
Our study reported significant loss in the ability to identify essential hypertension cases due to use of
free-text coding. However, the study methods and tools offer translational opportunities at the primary
care level, enabling each participating site to use these methods and tools to improve their own office
procedures, training, and policies surrounding data entry into EHRs. This study highlights the need for
training in data quality and management, even on basic levels such as using EHR templates and discrete
fields for data entry rather than free-text fields. Targeted training is advisable because various members of
a care team, such as physicians, nurses, medical assistants, and front-office staff, contribute data to the
EHR at various points in the care process. Continued monitoring of these sites using tools developed in
this research will help determine the long-term benefits of increased attention to EHR data quality. It is
reasonable to expect that efforts to improve data quality will bolster improved integration of these
systems while also facilitating the use of EHRs for quality-of-care improvement and efforts in practice
redesign.
This study points to the need for future research. First, only essential hypertension was studied.
Additional health conditions, such as other forms of hypertension, comorbid cardiovascular health
conditions, diabetes, or chronic kidney disease, need to be examined. Second, the de-identified data are
from only one EHR system. Future research needs to account for data from other systems to see if these
findings are replicated. Third, patients with consistently high blood pressure readings need clinical
follow-up to determine whether or not they have hypertension. Lastly, additional analyses are needed to
account for patient age criteria (i.e., 25–79 years of age) when identifying patients with hypertension. The
intent of this research was to identify patients with essential hypertension regardless of age or
demographic criteria, thereby permitting initial exploration of the ability to conduct a more rigorous level
of analysis of EHR data through importation of data into an external system.
Acknowledgments
The authors would like to acknowledge the support of ongoing partnership with the West Virginia
Bureau for Public Health, Office of Community Health Systems and Health Promotion.
Adam Baus, MA, MPH, is the senior program coordinator for the Office of Health Services Research
in the West Virginia University Department of Community Medicine in Morgantown, WV.
Michael Hendryx, PhD, is the director of the West Virginia Rural Health Research Center in
Morgantown, WV.
Cecil Pollard, MA, is the director of the Office of Health Services Research in the West Virginia
University Department of Community Medicine in Morgantown, WV.
Identifying Patients with Hypertension: A Case for Auditing Electronic Health Record Data
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78. Chronic Disease Electronic Management System (CDEMS). “The CDEMS User
Network.”Available at http://www.cdems.com.
Identifying Patients with Hypertension: A Case for Auditing Electronic Health Record Data
Table 1
Registry Data Fields Imported from the Electronic Health Record
Field Name
Description
patient_ID
Auto-generated ID used in the creation of de-identified data files
clinic_code
Used for subgrouping of patient populations (identifies site at which
care is being provided)
sex
Sex
ethnicity
Ethnicity
insurance
Insurance classification (i.e., Medicare, Medicaid, commercial
insurance, uninsured)
start_date
Date on which patient was added to the electronic record
migrant
Migrant status (Yes/No)
homeless
Homeless status (Yes/No)
raw_code (health)
Health condition
start_date (health)
Date on which diagnosis was added to the patient health profile
end_date (health)
Date on which diagnosis was archived in the patient
health profile
raw_code (lab)
Laboratory test
result (lab)
Laboratory result
service_date (lab)
Date on which lab result was received
raw_code (service)
Service
result (service)
Service result
service_date (service)
Date on which service was provided
raw_code (medication)
Medication
start_date (medication)
Date on which medication was prescribed
end_date (medication)
Date on which medication was discontinued
visit_date
Date on which office visit occurred
bp_systolic
Systolic blood pressure (linked to visit_date)
bp_diastolic
Diastolic blood pressure (linked to visit_date)
weight
Weight (linked to visit_date)
height
Height (linked to visit_date)
waist_circ
Waist circumference (linked to visit_date)
10 Perspectives in Health Information Management, Spring 2012
Table 2
Query Names and Descriptions
Sequence
Number
Description of the Query Function
1
Returns the number of patients with a diagnosis of essential hypertension in the
EHR by ICD-9-CM codes of 401.0–401.9, by site.
2
Builds on the prior query to return the number of unduplicated patients with a
diagnosis of essential hypertension in the EHR based on an ICD-9-CM code of
401.0–401.9, by site.
3
Returns the number of active patients with a diagnosis of “Other” and then free-text
documentation of essential hypertension in the EHR, by site. The following free-
text diagnoses were found:
HTN
HTN, ESSENTIAL
HTN since 2008
HTN .
HTN DIET CONTROLED
HTN CHONIC.
HISTORY OF HYPERTENSION
HIGH BLOODPRESSURE
HIGH BLOOD PRESSURE
HTN, mild
HIGH BLOOD PRESSURE
HX HYPERTENSION
HIGH BLOOD PRESSUER
HTN, UNCONTROLLED
HTN,UNTREATED
HTPERTENSION
HIGH BLLOD PRESSURE
HX HTN
DIASTOLIC HYPERTENSION
HX HYPERTENSION.
HX OF HTN
HX OF HYPERTENSION
HX OF HYPERTENTSION
HYEPRTENSION
HYERTENSION
HTPERTENSION WITH LVH
401.9 HYPERTENSION
HYPERTENSION
1. HYPERTENSION
401.0
401.01
401.1
401.1 HTN
401.1 HTN
Identifying Patients with Hypertension: A Case for Auditing Electronic Health Record Data
401.12
401.219
401.25
401.4
ELEVATED BP
401.9 HYPERTENSION
HIGH BLOOD PRESSURE
401.90
benigh essential HYPERTESION
BENIGN ESSENTIAL HYPERTENSION
BENIGN ESSENTIAL HYPERTENSION
BLOOD PRESSURE HIGH
HYPER TENSION
HYPEERTENSION
ESSENTIAL HYPERTENSION
HI NML HYPERTENSION
401.9
HYPERTESNION
HYPERTENSION/NEW.
HYPTERTENSION
HYPTERNSION
HYPRERTENSION
HYPPERTENSION
MILD HYPERTENSION
HYPERYTENSION
HYPERTENTISON
HYPERTENTION
HYPERTENSTION.
HYPERTENSTION
HYPERTENSON
HYHPERTENSION
HYPETENSION
SYSTOLIC HYPERTENSION
STABLE CARDIOMEGALY W/MILD VENOUS
HYPERTENSION
SIGNIFICANT FOR HYPERTENSION
Severe Hypertension
RESISTANT HYPERTENSION
Malignant Hypertension
HYPERTENSION/HI NML.
porly controlled HTN
HYPERTENISON
HYPERTENSION--
HYPERTENSION 2007
HYPERTENSION
HYPERTENSIOIN
HYPERTENSION ()
HYPERTENSIION, UNCONTROLLED
12 Perspectives in Health Information Management, Spring 2012
HYPERTENSION .
HYPERTENIOSN
HYPERTENION.
HYPERTENION
HYPERTEENSION UNCONTROLLED
hypert
HYPER TENSION.
HYPERTENSIN
HYPERTENSION UNCONTROLLED
HYPERTENSION/EPISODE.
HYPERTENSION/DIASTOLIC DYSTXN
HYPERTENSION/DIASTOLIC
HYPERTENSION.
HYPERTENSION, CONTROLLED
HYPERTENSION - STAGE II
HYPERTENSION W/ TACHY CARDIA
UNCONTROLLED HYPERTENSION
HYPERTENSION SYST.
HYPERTENSION NEW.
HYPERTENSION LVH
HYPERTENSION ESSENTIAL
HYPERTENSION CHR.
HYPERTENSION 401.9
HYPERTENSION WITH ELEVATED WEDP
4
Builds on the prior query to return the number of unduplicated patients with a free-
text diagnosis of essential hypertension, by site.
5
Locates duplicate patient records within the list of patients by ICD-9-CM code and
unduplicated patients by free text and determines the total number of unduplicated
patients with a diagnosis of essential hypertension, by site.
6
Returns a list of all blood pressure readings, by patient, that are greater than or
equal to 140/90 mm Hg, by site.
7
Builds on the prior query to return a list of all patients whose most recent two or
more blood pressure readings are greater than or equal to 140/90 mm Hg, by site.
8
Returns a list of patients whose most recent two or more blood pressure readings
are consistently greater than or equal to 140/90 mm Hg without a diagnosis of
essential hypertension by ICD-9-CM code or by free text, by site.
Identifying Patients with Hypertension: A Case for Auditing Electronic Health Record Data
Table 3
Count of Patients with Essential Hypertension, by Search Criteria
Search Criteria
Number
Number
Added
Cumulative
Percent
Patient count by ICD-9-CM code
12,919
--
86.7
Patient count by ICD-9-CM code plus free text
13,817
898
92.8
Patient count by ICD-9-CM code plus free text
plus last two or more blood pressure readings
greater than or equal to 140/90 mm Hg
14,893
1,076
100.0
Total
1,974
--
14 Perspectives in Health Information Management, Spring 2012
Table 4
Increase in Count of Patients with Essential Hypertension, by Search Criteria and Primary Care
Center
Primary
Care Center
A: Patients with
Hypertension:
ICD-9-CM
Coding
B: Patients with
Hypertension:
ICD-9-CM
Coding Plus Free
Text
C: Patients with
Hypertension: ICD-9-
CM Coding Plus Free
Text Plus Last 2+
Blood Pressure
Readings ≥140/90 mm
Hg
Percent
Missed Based
on ICD-9-CM
Coding Only
(100% − A/C)
A
5,124
5,270
5,535
7.4%
B
1,605
1,868
1,945
17.5%
C
476
505
596
20.1%
D
658
660
724
9.1%
E
852
859
884
3.6%
F
313
313
325
3.7%
G
228
418
438
47.9%
H
396
407
446
11.2%
I
666
714
749
11.1%
J
1,143
1,217
1,526
25.1%
K
1,458
1,586
1,725
15.5%
Sum
12,919
13,817
14,893
13.3%
Mean
1,174.45
1,256.09
1,353.91
Standard
Deviation
1,386.60
1,424.08
1,492.58
95% CI,
Lower
1,150.49
1,232.26
1,329.93
95% CI,
Upper
1,198.31
1,279.74
1,377.87
Identifying Patients with Hypertension: A Case for Auditing Electronic Health Record Data
Note: Figure shows statistically significant increases in identification of essential hypertension
cases using three search criteria methods. Electronic health record data are from all active
patients in each primary care center as of December 31, 2010.
... 9 Previous projects focusing on identifying undiagnosed hypertension have used various algorithms. [10][11][12][13] Most initiatives involved querying an electronic health record (EHR) to identify patients meeting specific criteria for elevated BP. Some initiatives also included strategies to recall identified patients to diagnose or rule out hypertension. ...
... One project focused on health centers but used a nonexperimental retrospective study design to examine EHR data for elevated BP and did not assess patients for hypertension. 11 Rakotz et al. used sophisticated algorithms that excluded patients with prehypertension and addressed BP variability. 12 That study tested three algorithms to determine which criteria identified the most patients at risk for undiagnosed hypertension; no single algorithm identified all at-risk patients. ...
... 12,13 Despite challenges with the structure, consistency, and completeness of EHR data, auditing these data for elevated BP readings (≥140 mmHg systolic or ≥ 90 mmHg diastolic) is an effective means to identify patients with hypertension who are "hiding in plain sight." [10][11][12][13]29 We found that diagnosed hypertension prevalence increased significantly from baseline to project end (34.5% in January 2015 vs. 36.7% in June 2016); this increase suggests that health centers can successfully use an algorithmbased approach to identify undiagnosed hypertension patients, confirm elevated BP, and diagnose appropriate patients with hypertension. Diagnosed hypertension prevalence increased despite the fact that participating health centers Undiagnosed Hypertension Longitudinal Study Group by Follow-Up Visit and Hypertension Diagnosis experienced an 7.1% increase in adult patients, which was likely due to Medicaid-expansion legislation in Arkansas, California, and Kentucky, which opened up health insurance coverage for uninsured adults who were previously excluded from Medicaid. ...
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Background: Hypertension is the most prevalent chronic condition diagnosed among patients served in the safety net in the United States; however, many safety-net patients with hypertension are not formally diagnosed and may remain untreated and at increased risk for cardiovascular events. Identifying undiagnosed hypertension using algorithmic logic programmed into clinical decision support (CDS) approaches is a promising practice but has not been broadly tested in the safety-net setting. Methods: The project used a quality improvement approach wherein information flows and actions related to blood pressure measurement were modified to include algorithm criteria to identify patients who might have undiagnosed hypertension. Identified patients were recalled for evaluation and hypertension diagnosis, if appropriate. Ten health centers in Arkansas, California, Kentucky, and Missouri were selected to participate in the project on the basis of high hypertension prevalence (compared to national average), demographic and geographic diversity, mature information systems infrastructure, and executive support. The project targeted patients from 18 to 85 years of age. Results: After implementation of algorithm-based interventions, diagnosed hypertension prevalence increased significantly from 34.5% to 36.7% (p <0.05). A cohort of patients was tracked from 8 of the 10 health centers to assess follow-up evaluation and diagnosis rates; 65.2% completed a follow-up evaluation, of which 31.9% received a hypertension diagnosis. Conclusion: Using algorithmic logic and other CDS-enabled care process improvements appears to be an effective way health centers can identify and engage patients at risk for undiagnosed hypertension. Appropriately diagnosing all hypertensive patients ensures that hypertension control efforts yield maximal improvements in population health.
... Li et al. 63 reported that of 2609 cases detected, MedLEE found 1253 (48%) that were not retrieved by searching International Classification of Diseases version-9 codes. A further five studies reported an increase in the number of cases found by using text, including for cancer, 64 hypertension, 65 inflammatory bowel disease, 66 ischemic stroke, 63 and disorders of sex development in children. 67 These studies reported a statistically significant increase in cases (P ¼ .003), ...
... 67 These studies reported a statistically significant increase in cases (P ¼ .003), 64 a 7-12% increase in cases, 65,66 226 patients being found using keyword search compared to 14 with manual search, 67 and 702 more patients found using text than with codes alone. 63 ...
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... The multidisciplinary WVHAMP team includes health services researchers with expertise in datainformed quality improvement and practice-based research within the WVU School of Public Health Office of Health Services Research; [35][36][37][38][39] infectious diseases clinicians in the WVU School of Medicine Departments of Medicine and Behavioral Medicine and Psychiatry with expertise in the infectious diseases associated with injection drug use; 40-44 a nurse with expertise in HCV treatment and systems approaches to HCV healthcare delivery; 45 and leadership from the West Virginia Rural Health Association that provides infrastructure to support WVHAMP. 46 This study aims to describe the role of WVHAMP in supporting team-based care for curing HCV through optimizing HIT designed to support real-time communication between providers and specialists, develop provider-level patient registries, and facilitate statewide surveillance for the goal of HCV elimination. ...
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This case study describes use of health information technology for enhanced team-based care and care coordination between primary care providers and infectious disease specialists for curing and eventually eliminating hepatitis C in West Virginia. This program, the West Virginia Hepatitis Academic Mentoring Partnership, aims to improve outcomes of West Virginians with chronic hepatitis C infection by training and supporting primary care providers to screen, diagnose, evaluate, treat, cure, and follow patients in the community rather than referring them to distant specialists with long wait times. This initiative supports health equity by increasing access to quality care in severely under-resourced rural areas. Primary care providers engage with hepatitis C experts in a web-based training and mentoring process, combined with informatics training in use of a customized Research Electronic Data Capture (REDCAP) platform for secure data tracking and bidirectional communication. This use of an informatics platform available to all partners supports shared decision-making between primary care providers and specialists, fostering a primary care learning network for improved hepatitis C care in West Virginia.
... . Larger practices tended to have stronger organizational resources, which led to more internal technical support staff to assist with developing workflow and process changes, financial resources and HIT support staff27 .Federally Qualified Health Centers are required as grant recipient clinics to report program data annually for key measures that evaluate access, quality, outcomes and costs through the Bureau of Primary Health Care (BPHC) Health Resources and Services Administration's (HRSA) Uniform Data System (UDS) in order to monitor a health centers' performance and86 . Patient volume is determined based on the unduplicated numbers of individuals encountered by each parent organization, an approach used as a common size metric in studies involving patient level analysis and HIT[94][95][96][97][98][99] . Previous studies have not evaluated patient volume as an independent variable related to FQHCs and adoption of Technology. ...
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... W39XXXA and 2) by a free text search of patients' entire electronic medical records for phrases and parts of phrases including, "firework, firecracker, class c, bottle rocket, rocket, roman candle, sparkler, sparkling devices, pyrotechnic, explosive, snap-cap, snappers, cherry bomb, trick noisemakers, missile type rocket, smoke device." This combination of searching for patients via ICD codes and free text is far superior to searching by ICD code alone and has been shown to identify more patients for study [22]. ...
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... Transformation of the data files was performed with a Microsoft Access-based clinical information system. 55 This tool is open-source, public domain software shown effective in previous research analyzing EHR data for diagnostic coding 56 and identifying patients at risk of diabetes. 57 Data were de-identified using the Safe Harbor method of data de-identification. ...
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Electronic health records (EHRs) are increasingly used by US outpatient physicians. They could improve clinical care via clinical decision support (CDS) and electronic guideline-based reminders and alerts. Using nationally representative data, we tested the hypothesis that a higher quality of care would be associated with EHRs and CDS. We analyzed physician survey data on 255,402 ambulatory patient visits in nonfederal offices and hospitals from the 2005-2007 National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey. Based on 20 previously developed quality indicators, we assessed the relationship of EHRs and CDS to the provision of guideline-concordant care using multivariable logistic regression. Electronic health records were used in 30% of an estimated 1.1 billion annual US patient visits. Clinical decision support was present in 57% of these EHR visits (17% of all visits). The use of EHRs and CDS was more likely in the West and in multiphysician settings than in solo practices. In only 1 of 20 indicators was quality greater in EHR visits than in non-EHR visits (diet counseling in high-risk adults, adjusted odds ratio, 1.65; 95% confidence interval, 1.21-2.26). Among the EHR visits, only 1 of 20 quality indicators showed significantly better performance in visits with CDS compared with EHR visits without CDS (lack of routine electrocardiographic ordering in low-risk patients, adjusted odds ratio, 2.88; 95% confidence interval, 1.69-4.90). There were no other significant quality differences. Our findings indicate no consistent association between EHRs and CDS and better quality. These results raise concerns about the ability of health information technology to fundamentally alter outpatient care quality.
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Complete documentation in anaesthetic records is important for patient management, research and quality assurance and has medicolegal implications. This study compares the completeness of information contained in electronic versus handwritten intraoperative anaesthetic records. A sample of 70 handwritten records was randomly selected from anaesthesia performed in the month prior to implementation of the Integrated Injectable Drug Administration and Automated Anaesthesia Record System and compared to a similar sample of electronic records generated eight months later. A comprehensive scoring system, based on the Australian and New Zealand College of Anaesthetists’ guideline PS6, was used to compare the completeness of information throughout the entire intraoperative record. There was no significant difference in the total score for completeness between electronic (78%) and handwritten (83%) records (P=0.16). Handwritten records were more complete with respect to weight (P <0.0001), American Society of Anesthesiologists’ physical status score (P <0.0001), the size and type of artificial airway used (P=0.003) and a record of the surgeons involved (P=0.0004). Electronic records were more complete with respect to a record of drug administration including intravenous drugs (P <0.0001), vapour (P=0.0001) and nitrous oxide/oxygen (P <0.0001), a record of end-tidal carbon dioxide monitoring (P=0.006) and the level of trainee supervision (P=0.0002). There was no overall difference in the completeness of electronic versus handwritten records. Several differences did exist however, highlighting both clinically important advantages and deficiencies in the electronic system. Records from both systems sometimes lacked important information.