scholarly journals Use of Health Information Exchange Improved the Identification of Healthcare-Associated Group A Streptococcus Infections

2020 ◽  
Vol 41 (S1) ◽  
pp. s423-s423
Author(s):  
Alana Cilwick ◽  
Alexis Burakoff ◽  
Wendy Bamberg ◽  
Geoffrey Brousseau ◽  
Nisha Alden ◽  
...  
Keyword(s):  
Public Health ◽  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Healthcare Facilities ◽  
Associated Gas ◽  
Case Ascertainment ◽  
Outbreak Investigations ◽  
Group A ◽  
Healthcare Associated

Background: Healthcare-associated group A Streptococcus (GAS) infections can cause severe morbidity and death. Invasive GAS is a reportable condition in the 5-county metropolitan area of Denver, Colorado. Prior to August 2018, methodology to identify long-term care facility (LTCF) residency among reported GAS cases was accomplished by reviewing addresses reported electronically, and identification of postsurgical cases and outbreaks relied on reporting by healthcare facilities. We evaluated whether the use of a health information exchange (HIE) to identify healthcare exposures improved our ability to detect and rapidly respond to these events. Methods: In August 2018, we implemented a review of health records available in the HIE accessible by the Colorado Department of Public Health and Environment for all incoming reports of GAS for selected healthcare exposures: LTCF residency, surgery, delivery, wound care, and other relevant exposures. We defined an LTCF-related case as GAS in a current or recent resident (ie, in the 14 days prior to the positive culture) of an LTCF. Postpartum and postsurgical cases were defined as GAS isolated from a sterile site or wound during the inpatient stay or within 7 days of discharge following a delivery or surgical procedure. Outbreaks in each of these settings were defined as 2 or more cases within a 3-month period. We compared the number of cases and outbreaks identified in each category during a 1-year period before and after implementation of the use of the HIE in the case ascertainment process. Results: During August 2017 through July 2018, prior to implementation of the HIE process, we detected 45 LTCF cases and conducted outbreak investigations in 9 facilities. Moreover, 1 postsurgical case and 1 postpartum outbreak were reported by healthcare facilities; none were detected via surveillance. During August 2018 through July 2019, after the implementation of HIE process, we identified 70 LTCF cases and conducted outbreak investigations in 13 LTCFs. We detected 5 postsurgical cases and 3 postpartum cases, which resulted in 2 outbreak investigations. Conclusions: Enhanced GAS surveillance through use of a HIE resulted in detection of more healthcare-associated GAS infections and outbreaks. Timely identification of healthcare-associated GAS infections can allow for prompt response to outbreaks and promotion of proper infection control practices to prevent further cases. Jurisdictions in which GAS is a reportable condition should consider the use of HIEs as part of routine surveillance to identify GAS outbreaks in high-risk settings. HIEs should be made available to public health agencies for case ascertainment and outbreak identification.Funding: NoneDisclosures: None

scholarly journals Measuring the Degree of Unmatched Patient Records in a Health Information Exchange Using Exact Matching

2016 ◽  
Vol 07 (02) ◽  
pp. 330-340 ◽  
Author(s):  
John Zech ◽  
Gregg Husk ◽  
Thomas Moore ◽  
Jason Shapiro
Keyword(s):  
Social Security ◽  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Reference Value ◽  
P Value ◽  
Patient Records ◽  
Exact Matching ◽  
Healthcare Facilities ◽  
Significant Difference

SummaryHealth information exchange (HIE) facilitates the exchange of patient information across different healthcare organizations. To match patient records across sites, HIEs usually rely on a master patient index (MPI), a database responsible for determining which medical records at different healthcare facilities belong to the same patient. A single patient’s records may be improperly split across multiple profiles in the MPI.We investigated the how often two individuals shared the same first name, last name, and date of birth in the Social Security Death Master File (SSDMF), a US government database containing over 85 million individuals, to determine the feasibility of using exact matching as a split record detection tool. We demonstrated how a method based on exact record matching could be used to partially measure the degree of probable split patient records in the MPI of an HIE.We calculated the percentage of individuals who were uniquely identified in the SSDMF using first name, last name, and date of birth. We defined a measure consisting of the average number of unique identifiers associated with a given first name, last name, and date of birth. We calculated a reference value for this measure on a subsample of SSDMF data. We compared this measure value to data from a functioning HIE.We found that it was unlikely for two individuals to share the same first name, last name, and date of birth in a large US database including over 85 million individuals. 98.81% of individuals were uniquely identified in this dataset using only these three items. We compared the value of our measure on a subsample of Social Security data (1.00089) to that of HIE data (1.1238) and found a significant difference (t-test p-value < 0.001).This method may assist HIEs in detecting split patient records.

scholarly journals Health Information Exchange: A Novel Re-linkage Intervention in an Urban Health System

2019 ◽  
Vol 6 (10) ◽  
Author(s):  
Joseph Sharp ◽  
Christine D Angert ◽  
Tyania Mcconnell ◽  
Pascale Wortley ◽  
Eugene Pennisi ◽  
...  
Keyword(s):  
Public Health ◽  
Health System ◽  
Health Information ◽  
Information Exchange ◽  
Viral Suppression ◽  
Health Information Exchange ◽  
Linkage To Care ◽  
Primary Analysis ◽  
Adjusted Risk ◽  
Hiv 1

Abstract Background Public health information exchanges (HIEs) link real-time surveillance and clinical data and can help to re-engage out-of-care people with HIV (PWH). Methods We conducted a retrospective cohort study of out-of-care PWH who generated an HIE alert in the Grady Health System (GHS) Emergency Department (ED) between January 2017 and February 2018. Alerts were generated for PWH who registered in the GHS ED without Georgia Department of Public Health (GDPH) CD4 or HIV-1 RNA in the prior 14 months. The alert triggered a social work (SW)–led re-linkage effort. Multivariate logistic regression analyses used HIE-informed SW re-linkage efforts as the independent variable, and linkage to care and 3- and 6-month viral suppression (HIV-1 RNA &lt; 200 c/mL) as primary outcomes. Patients admitted to the hospital were excluded from primary analysis. Results One hundred forty-seven out-of-care patients generated an alert. Ninety-eight were included in the primary analysis (mean age [SD], 41 ± 12 years; 70% male; 93% African American), and 20 received the HIE-informed SW intervention. Sixty percent of patients receiving the intervention linked to care in 6 months, compared with 35% who did not. Patients receiving the intervention were more likely to link to care (adjusted risk ratio [aRR], 1.63; 95% confidence interval [CI], 0.99–2.68) and no more likely to achieve viral suppression (aRR, 1.49; 95% CI, 0.50–4.46) than those who did not receive the intervention. Conclusions An HIE-informed, SW-led intervention systematically identified out-of-care PWH and may increase linkage to care for this important population. HIEs create an opportunity to intervene with linkage and retention strategies.

scholarly journals Surveillance of Stillbirth and Syphilis Screening Using Electronic Health Records

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Brian E Dixon ◽  
Jane Wang ◽  
Timothy E O'Connor ◽  
Janet N Arno
Keyword(s):  
Public Health ◽  
Electronic Health Records ◽  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Congenital Syphilis ◽  
The United States ◽  
Health Records ◽  
Syphilis Testing ◽  
Electronic Health

Objective: To measure stillbirth delivery rates and syphilis screening rates among women with a stillbirth delivery using electronic health record data available in a health information exchange.Introduction: Reports of infants born with congenital syphilis have increased in the United States every year since 2012. Prevention depends on high performing surveillance systems and compliance with the U.S. Centers for Disease Control and Prevention (CDC) recommendations to perform syphilis testing early in pregnancy, in the third trimester and at delivery if a woman is at high risk, and following a stillbirth delivery. These guidelines exist, because untreated syphilis is associated with adverse fetal outcomes including central nervous system infection and death.Surveillance of congenital syphilis and stillbirth is challenging because available data sources are limited. Assessment of compliance with testing guidelines is particularly challenging, since public health agencies often lack access to comprehensive cohorts of tested individuals as most public health laws only require reporting of positive disease case information.Methods: Using integrated electronic health records available in a community-based health information exchange, we examined syphilis testing patterns for women with a stillbirth delivery in Indiana between 2010-2016. The cohort was examined to determine whether the women received syphilis testing in accordance with the CDC recommendations. During this time period, Indiana recorded around 84,000 live births per year.Data were extracted from electronic health records, including encounter data, laboratory test results and procedure data, captured by the Indiana Network for Patient Care (INPC), one of the largest community-based HIE networks in the United States. The INPC connects over 90 health care facilities, including hospitals, physicians’ practices, pharmacy networks, long-term post-acute care facilities, laboratories, and radiology centers. In addition to clinical care, the INPC supports surveillance of STIs1.Women with a stillbirth delivery were identified using International Classification of Disease (ICD) Clinical Modification (CM) codes from the 9thand 10th editions (ICD-CM-9 and ICD-CM-10). Inclusion codes: ICD-CM-9 codes 656.4, 779.9, V27.1, V27.3, V27.4, V27.6, V27.7, V32.01, V32.1, V32.2, V36.1; and ICD-CM-10 codes P95, P96.9, O36.4, Z37.1, Z37.3, Z37.4, Z37.9.Using the master person index for the INPC, we linked stillbirth deliveries with pregnancy encounters and laboratory testing data. We analyzed documentation of syphilis testing during the pregnancy (up to 270 days prior to the stillbirth delivery) as well as after the stillbirth delivery (up to 30 days). Broad time ranges were utilized to account for potential delays in reporting of either the stillbirth delivery or the syphilis test results. Documentation could include either presence of a result from a laboratory test for syphilis or a CPT code (80055, 86780, 86781, 86592, 86593) indicating performance of a syphilis test.Results: A total of 4,361 stillbirth deliveries attributable to 4,265 unique women were identified in the INPC between 2010-2016; representing a rate of 7.44 stillbirths per 1,000 live births during the same time period. Of the stillbirth deliveries, syphilis testing occurred within 270 days prior to or 30 days after delivery for 2,763 (63.4%) cases. Figure 1 displays the number of stillbirth cases observed each year and the number of cases in which syphilis testing occurred during the pregnancy or after delivery.Conclusions: Using integrated electronic health records data, we discovered that fetal deaths occurred more frequently (7.44 versus 4.09 per 1,000) than previously estimated2 through fetal death reporting mechanisms in Indiana. Furthermore, we observed increasing rates of stillbirth within Indiana in recent years. Integrated data further enabled measurement of syphilis testing rates for stillbirth cases, which were similar to those reported by Patel et al.3using a large, national administrative data set. Testing rates in Indiana are well below the targets set by national and international public health organizations. Accessing more complete data on populations using a health information exchange is valuable, although doing so may uncover a more negative picture of health in one’s community. Deeper analysis of these trends is warranted to explore factors related to increasing rates as well as limited testing in this population.

scholarly journals Leveraging public health's participation in a Health Information Exchange to improve communicable disease reporting

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Ian Painter ◽  
Debra Revere ◽  
P. Joseph Gibson ◽  
Janet Baseman
Keyword(s):  
Public Health ◽  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Communicable Disease ◽  
Health Agency ◽  
Public Health Agency ◽  
Public Health Agencies ◽  
The Public ◽  
The Mean

Background: Infectious diseases can appear and spread rapidly. Timely information about disease patterns and trends allows public health agencies to quickly investigate and efficiently contain those diseases. But disease case reporting to public health has traditionally been paper-based, resulting in somewhat slow, burdensome processes. Fortunately, the expanding use of electronic health records and health information exchanges has created opportunities for more rapid, complete, and easily managed case reporting and investigation. To assess how this new service might impact the efficiency and quality of a public health agency's case investigations, we compared the timeliness of usual case investigation to that of case investigations based on case report forms that were partially pre-populated with electronic data. Intervention: Between September 2013-March 2014, chlamydia disease report forms for certain clinics in Indianapolis were electronically pre-populated with clinical, lab and patient data available through the Indiana Health Information Exchange, then provided to the patient’s doctor. Doctors could then sign the form and deliver it to public health for investigation and population-level disease tracking. Methods: We utilized a novel matched case analysis of timeliness changes in receipt and processing of communicable disease report forms. Each Chlamydia cases reported with the pre-populated form were matched to cases reported in usual ways. We assessed the time from receipt of the case at the public health agency: 1) inclusion of the case into the public health surveillance system and 2) to close to case. A hierarchical random effects model was used to compare mean difference in each outcome between the target cases and the matched cases, with random intercepts for case. Results: Twenty-one Chlamydia cases were reported to the public health agency using the pre-populated form. Sixteen of these pre-populated form cases were matched to at least one other case, with a mean of 23 matches per case. The mean Reporting Lag for the pre-populated form cases was 2.5 days, which was 2.7 days shorter than the mean Reporting Lag for the matched controls (p = <0.001). The mean time to close a pre-populated form case was 4.7 days, which was 0.2 days shorter than time to close for the matched controls (p = 0.792). Conclusions: Use of pre-populated forms significantly decreased the time it took for the local public health agency to begin documenting and closing chlamydia case investigations. Thoughtful use of electronic health data for case reporting may decrease the per-case workload of public health agencies, and improve the timeliness of information about the pattern and spread of disease.

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