National Healthcare Quality and Disparities Report
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Topics
- Adverse Drug Events (ADE) (3)
- Adverse Events (2)
- Autism (1)
- Cancer (3)
- Cardiovascular Conditions (1)
- Children/Adolescents (3)
- Chronic Conditions (2)
- Clinical Decision Support (CDS) (2)
- Clinician-Patient Communication (1)
- Communication (1)
- Comparative Effectiveness (1)
- (-) Data (31)
- Decision Making (1)
- Diagnostic Safety and Quality (1)
- (-) Electronic Health Records (EHRs) (31)
- Family Health and History (1)
- Genetics (2)
- Healthcare-Associated Infections (HAIs) (1)
- Healthcare Cost and Utilization Project (HCUP) (1)
- Healthcare Delivery (1)
- Health Information Exchange (HIE) (1)
- Health Information Technology (HIT) (22)
- Health Services Research (HSR) (1)
- Heart Disease and Health (3)
- Hospitals (2)
- Imaging (1)
- Injuries and Wounds (1)
- Medication (4)
- Medication: Safety (1)
- Mortality (1)
- Nursing (1)
- Patient-Centered Healthcare (3)
- Patient-Centered Outcomes Research (2)
- Patient Safety (2)
- Prevention (1)
- Provider: Pharmacist (1)
- Public Health (1)
- Quality Improvement (1)
- Quality of Care (1)
- Racial and Ethnic Minorities (3)
- Registries (3)
- Research Methodologies (1)
- Risk (1)
- Sepsis (1)
- Surgery (2)
- Vitamins and Supplements (1)
AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 25 of 31 Research Studies DisplayedByrd TF, Ahmad FS, Liebovitz DM
Defragmenting heart failure care: medical records integration.
This article discusses the need to improve interoperability of software systems so that so that providers and patients can access clinical information needed to help coordinate care of heart failure patients. New data standards currently being proposed in legislation would make it possible to guide clinical decision-making.
AHRQ-funded; HS026385.
Citation: Byrd TF, Ahmad FS, Liebovitz DM .
Defragmenting heart failure care: medical records integration.
Heart Fail Clin 2020 Oct;16(4):467-77. doi: 10.1016/j.hfc.2020.06.007..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Heart Disease and Health, Cardiovascular Conditions, Data
Bacon E, Budney G, Bondy J
Developing a regional distributed data network for surveillance of chronic health conditions: the Colorado Health Observation Regional Data Service.
This article describes attributes of regional distributed data networks using electronic health records (EHR) data and the history and design of Colorado Health Observation Regional Data Service as an emerging public health surveillance tool for chronic health conditions. The authors indicate that while benefits from EHR-based surveillance are described, a number of technology, partnership, and value proposition challenges remain.
AHRQ-funded; HS0122143.
Citation: Bacon E, Budney G, Bondy J .
Developing a regional distributed data network for surveillance of chronic health conditions: the Colorado Health Observation Regional Data Service.
J Public Health Manag Pract 2019 Sep/Oct;25(5):498-507. doi: 10.1097/phh.0000000000000810..
Keywords: Chronic Conditions, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Public Health
Liu L, Ni Y, Zhang N
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
The objectives of this study were: 1) to develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children's risk of day-of-surgery cancellation. The study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The author’s approach offers the promise of targeted interventions to significantly decrease both healthcare costs and families' negative experiences.
AHRQ-funded; HS024983.
Citation: Liu L, Ni Y, Zhang N .
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
Int J Med Inform 2019 Sep;129:234-41. doi: 10.1016/j.ijmedinf.2019.06.007..
Keywords: Children/Adolescents, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery
Polubriaginof FCG, Ryan P, Salmasian H
Challenges with quality of race and ethnicity data in observational databases.
This study assessed the quality of race and ethnicity information in observational health databases as well as electronic health records (EHRs) and to propose patient self-recording as a way to improve accuracy. Data from the Healthcare Cost and Utilization Project (HCUP) and Optum Labs, and from a single New York City healthcare system’s EHR was compared. Among 160 million patients in the HCUP database, no race or ethnicity data was recorded for 25% of the records. Among the 2.4 million patients in the New York City HER, race or ethnicity was unknown for 57%. However, when patients were allowed to directly record their race and ethnicity, percentages rose to 86%.
AHRQ-funded; HS021816; HS023704; HS024713.
Citation: Polubriaginof FCG, Ryan P, Salmasian H .
Challenges with quality of race and ethnicity data in observational databases.
J Am Med Inform Assoc 2019 Aug;26(8-9):730-36. doi: 10.1093/jamia/ocz113..
Keywords: Healthcare Cost and Utilization Project (HCUP), Data, Racial and Ethnic Minorities, Electronic Health Records (EHRs), Health Information Technology (HIT), Health Services Research (HSR)
Polubriaginof FCG, Vanguri R, Quinnies K
Disease heritability inferred from familial relationships reported in medical records.
Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. This study used EHR data to compute heritability estimates for 500 disease phenotypes. These analyses provided a validation of the use of EHRs for genetics and disease research.
AHRQ-funded; HS021816; HS022961.
Citation: Polubriaginof FCG, Vanguri R, Quinnies K .
Disease heritability inferred from familial relationships reported in medical records.
Cell 2018 Jun 14;173(7):1692-704.e11. doi: 10.1016/j.cell.2018.04.032..
Keywords: Data, Family Health and History, Genetics, Health Information Technology (HIT), Electronic Health Records (EHRs)
Goss FR, Lai KH, Topaz M
A value set for documenting adverse reactions in electronic health records.
In this study, the investigators developed a value set for encoding adverse reactions using a large dataset from one health system, enriched by reactions from 2 large external resources. This integrated value set included clinically important severe and hypersensitivity reactions. The work contributed a value set, harmonized with existing data, to improve the consistency and accuracy of reaction documentation in electronic health records, providing the necessary building blocks for more intelligent clinical decision support for allergies and adverse reactions.
AHRQ-funded; HS022728.
Citation: Goss FR, Lai KH, Topaz M .
A value set for documenting adverse reactions in electronic health records.
J Am Med Inform Assoc 2018 Jun;25(6):661-69. doi: 10.1093/jamia/ocx139..
Keywords: Adverse Drug Events (ADE), Adverse Events, Electronic Health Records (EHRs), Medication, Data, Health Information Technology (HIT), Patient Safety
Rhee C, Dantes R, Epstein L
Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014.
The researchers estimated the US national incidence of sepsis and trends using detailed clinical data from the electronic health record (EHR) systems of diverse hospitals. In clinical data from 409 hospitals, sepsis was present in 6 percent of adult hospitalizations, and in contrast to claims-based analyses, neither the incidence of sepsis nor the combined outcome of death or discharge to hospice changed significantly between 2009-2014.
AHRQ-funded; HS025008.
Citation: Rhee C, Dantes R, Epstein L .
Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014.
JAMA 2017 Oct 3;318(13):1241-49. doi: 10.1001/jama.2017.13836.
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Keywords: Data, Electronic Health Records (EHRs), Hospitals, Mortality, Sepsis
Ong TC, Kahn MG, Kwan BM
Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading.
The researchers designed and implemented a health data transformation and loading approach, which we refer to as Dynamic ETL (Extraction, Transformation and Loading) (D-ETL), that automates part of the process through use of scalable, reusable and customizable code. Their results showed that ETL rule composition methods and the D-ETL engine offer a scalable solution for health data transformation via automatic query generation to harmonize source datasets.
AHRQ-funded; HS019908; HS022956.
Citation: Ong TC, Kahn MG, Kwan BM .
Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading.
BMC Med Inform Decis Mak 2017 Sep 13;17(1):134. doi: 10.1186/s12911-017-0532-3.
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Keywords: Comparative Effectiveness, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient-Centered Outcomes Research
Rowan CG, Flory J, Gerhard T
Agreement and validity of electronic health record prescribing data relative to pharmacy claims data: a validation study from a US electronic health record database.
The researchers conducted a retrospective cohort study among patients with linked claims and EHR data in OptumLabs Data Warehouse. Their aim was to evaluate the validity of classifying medication exposure using EHR prescribing (EHR-Rx) data. They concluded that, despite substantial variability among different medications, there was very good agreement between EHR-Rx data and PC-Rx data.
AHRQ-funded; HS023898.
Citation: Rowan CG, Flory J, Gerhard T .
Agreement and validity of electronic health record prescribing data relative to pharmacy claims data: a validation study from a US electronic health record database.
Pharmacoepidemiol Drug Saf 2017 Aug;26(8):963-72. doi: 10.1002/pds.4234.
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Keywords: Data, Electronic Health Records (EHRs), Medication, Provider: Pharmacist
Bush RA, Connelly CD, Perez A
Extracting autism spectrum disorder data from the electronic health record.
This study uses electronic health record (EHR) data to examine medical utilization and track outcomes among children with Autism Spectrum Disorder (ASD). The study also identifies challenges inherent in designing inclusive algorithms for identifying individuals with ASD and demonstrates the utility of employing multiple extractions to improve the completeness and quality of EHR data when conducting research.
AHRQ-funded; HS022404.
Citation: Bush RA, Connelly CD, Perez A .
Extracting autism spectrum disorder data from the electronic health record.
Appl Clin Inform 2017 Jul 19;8(3):731-41. doi: 10.4338/aci-2017-02-ra-0029..
Keywords: Autism, Children/Adolescents, Data, Health Information Technology (HIT), Electronic Health Records (EHRs)
Lybarger K, Ostendorf M, Yetisgen M
Automatically detecting likely edits in clinical notes created using automatic speech recognition.
Aiming to reduce the time required to edit automatic speech recognition (ASR) transcripts, this paper investigates novel methods for automatic detection of edit regions within the transcripts, including both putative ASR errors but also regions that are targets for cleanup or rephrasing.
AHRQ-funded; HS023631.
Citation: Lybarger K, Ostendorf M, Yetisgen M .
Automatically detecting likely edits in clinical notes created using automatic speech recognition.
AMIA Annu Symp Proc 2017 Apr 16;2017:1186-95.
Keywords: Health Information Technology (HIT), Electronic Health Records (EHRs), Data
Hu Z, Melton GB, Arsoniadis EG
Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for postoperative complication detection is unclear. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values.
AHRQ-funded; HS024532.
Citation: Hu Z, Melton GB, Arsoniadis EG .
Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
J Biomed Inform 2017 Apr;68:112-20. doi: 10.1016/j.jbi.2017.03.009.
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Keywords: Data, Electronic Health Records (EHRs), Healthcare-Associated Infections (HAIs), Registries, Surgery, Injuries and Wounds, Health Information Technology (HIT), Quality Improvement, Quality of Care, Adverse Events
Blumenthal KG, Acker WW, Li Y
Allergy entry and deletion in the electronic health record.
The researchers aimed to assess drug allergy entry, deletion, and accumulation, to identify health care professional types recording allergy data, and to assess the reaction types that lead to allergy entry and deletion. They found that of all allergies, 38.2 percent were immune mediated and 29.6 percent included only adverse effect reactions. Unavailable or unknown reactions comprised 32.2 percent of all allergies entered or deleted.
AHRQ-funded; HS022728.
Citation: Blumenthal KG, Acker WW, Li Y .
Allergy entry and deletion in the electronic health record.
Ann Allergy Asthma Immunol 2017 Mar;118(3):380-81. doi: 10.1016/j.anai.2016.12.020.
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Keywords: Data, Electronic Health Records (EHRs), Medication: Safety, Medication, Adverse Drug Events (ADE)
Roosan D, Samore M, Jones M
Big-data based decision-support systems to improve clinicians' cognition.
This study focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. It found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians' cognition to deal with complex problems. These cognitive strategies all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records.
AHRQ-funded; HS023349.
Citation: Roosan D, Samore M, Jones M .
Big-data based decision-support systems to improve clinicians' cognition.
IEEE Int Conf Healthc Inform 2016;2016:285-88. doi: 10.1109/ichi.2016.39.
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Keywords: Clinical Decision Support (CDS), Decision Making, Data, Electronic Health Records (EHRs)
Murphy DR, Meyer AN, Bhise V
Computerized triggers of big data to detect delays in follow-up of chest imaging results.
A "trigger" algorithm was used to identify delays in follow-up of abnormal chest imaging results in a large national clinical data warehouse of electronic health record (EHR) data. In this study, the authors applied a trigger in a repository hosting EHR data from all Department of Veterans Affairs health-care facilities and analyzed data from seven facilities. The investigators concluded that application of triggers on "big" EHR data may aid in identifying patients experiencing delays in diagnostic evaluation of chest imaging results suspicious for malignancy.
Citation: Murphy DR, Meyer AN, Bhise V .
Computerized triggers of big data to detect delays in follow-up of chest imaging results.
Chest 2016 Sep;150(3):613-20. doi: 10.1016/j.chest.2016.05.001..
Keywords: Imaging, Electronic Health Records (EHRs), Health Information Technology (HIT), Data, Diagnostic Safety and Quality, Cancer
Richesson RL, Sun J, Pathak J
Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.
The authors sought to use electronic health records data to advance understanding of disease risk and drug response, and to support the practice of precision medicine on a national scale. They found that machine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, as it comes from data rather than experts. They suggested that research networks and phenotype developers cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and modernize biomedical research.
AHRQ-funded; HS023921; HS023077.
Citation: Richesson RL, Sun J, Pathak J .
Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.
Artif Intell Med 2016 Jul;71:57-61. doi: 10.1016/j.artmed.2016.05.005.
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Keywords: Data, Electronic Health Records (EHRs), Genetics, Patient-Centered Healthcare
Cato KD, Bockting W, Larson E
Did I tell you that? Ethical issues related to using computational methods to discover non-disclosed patient characteristics.
Using the Belmont Report's principles of respect for persons, beneficence, and justice as a framework, the authors examined the ethical issues posed by electronic phenotyping. Ethical issues identified include the ability of the patient to consent for the use of their information, the ability to suppress pediatric information, and ensuring that the potential benefits justify the risks of harm to patients.
AHRQ-funded; HS022961.
Citation: Cato KD, Bockting W, Larson E .
Did I tell you that? Ethical issues related to using computational methods to discover non-disclosed patient characteristics.
J Empir Res Hum Res Ethics 2016 Jul;11(3):214-9. doi: 10.1177/1556264616661611.
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Keywords: Clinician-Patient Communication, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient-Centered Outcomes Research, Registries, Research Methodologies
Vock DM, Wolfson J, Bandyopadhyay S
Adapting machine learning techniques to censored time-to-event health record data: a general-purpose approach using inverse probability of censoring weighting.
In this paper, the authors present a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW). They illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models.
AHRQ-funded; HS017622.
Citation: Vock DM, Wolfson J, Bandyopadhyay S .
Adapting machine learning techniques to censored time-to-event health record data: a general-purpose approach using inverse probability of censoring weighting.
J Biomed Inform 2016 Jun;61:119-31. doi: 10.1016/j.jbi.2016.03.009.
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Keywords: Data, Electronic Health Records (EHRs), Health Information Technology (HIT)
Lee SJ, Grobe JE, Tiro JA
Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals.
The objective of this study was to characterize the quality of race/ethnicity data collection efforts. The authors assessed race and ethnicity data quality across cancer registries and electronic medical records in two hospitals. Their findings suggested that high-quality race/ethnicity data are attainable. Many of the "errors" in race/ethnicity data were caused by missing or "Unknown" data values.
AHRQ-funded; HS022418.
Citation: Lee SJ, Grobe JE, Tiro JA .
Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals.
J Am Med Inform Assoc 2016 May;23(3):627-34. doi: 10.1093/jamia/ocv156..
Keywords: Cancer, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Hospitals, Racial and Ethnic Minorities, Registries
Plasek JM, Goss FR, Lai KH
Food entries in a large allergy data repository.
This study examined, encoded, and grouped foods that caused any adverse sensitivity in a large allergy repository using natural language processing and standard terminologies. It identified 158,552 food allergen records (2,140 unique terms) in the Partners repository, corresponding to 672 food allergen concepts. High-frequency groups included shellfish (19.3 percent), fruits or vegetables (18.4 percent), dairy (9.0 percent), and peanuts (8.5 percent).
AHRQ-funded; HS022728.
Citation: Plasek JM, Goss FR, Lai KH .
Food entries in a large allergy data repository.
J Am Med Inform Assoc 2016 Apr;23(e1):e79-87. doi: 10.1093/jamia/ocv128.
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Keywords: Data, Health Information Technology (HIT), Electronic Health Records (EHRs), Patient Safety
Hsu D, Brieva J, Nardone B
Validation of database search strategies for the epidemiological study of pemphigus and pemphigoid.
The authors hypothesized that the assigned ICD-9-CM codes of 694.4 (pemphigus) and 694.5 (pemphigoid) would demonstrate a high predictive value for the confirmed diagnosis of their respective diseases. Their results did not support the hypothesis that a single ICD-9-CM code for pemphigus and pemphigoid is sufficient to identify these disorders in largescale epidemiological studies.
AHRQ-funded; HS023011.
Citation: Hsu D, Brieva J, Nardone B .
Validation of database search strategies for the epidemiological study of pemphigus and pemphigoid.
Br J Dermatol 2016 Mar;174(3):645-8. doi: 10.1111/bjd.14172.
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Keywords: Data, Electronic Health Records (EHRs), Health Information Technology (HIT)
Panahiazar M, Taslimitehrani V, Pereira NL
Using EHRs for heart failure therapy recommendation using multidimensional patient similarity analytics.
The authors developed a multidimensional patient similarity assessment technique that leverages multiple types of information from the electronic health records and predicts a medication plan for each new patient based on prior knowledge and data from similar patients.Their findings suggest that it is feasible to harness population-based information for an individual patient-specific assessment.
AHRQ-funded; HS023077.
Citation: Panahiazar M, Taslimitehrani V, Pereira NL .
Using EHRs for heart failure therapy recommendation using multidimensional patient similarity analytics.
Stud Health Technol Inform 2015;210:369-73.
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Keywords: Clinical Decision Support (CDS), Data, Electronic Health Records (EHRs), Heart Disease and Health, Patient-Centered Healthcare
Zhang R, Manohar N, Arsoniadis E
Evaluating term coverage of herbal and dietary supplements in electronic health records.
Some supplements can interact with prescription medications, potentially leading to clinically important and potentially preventable adverse reactions. Clinical notes and corresponding medication lists from an integrated healthcare system were extracted and compared with online databases. The authors found that, overall, about 40% of listed medications are supplements, most of which are included in medication lists as nutritional or miscellaneous products. They found gaps between supplement and standard medication terminologies and identified supplements which were not mentioned in the medication lists.
AHRQ-funded; HS022085.
Citation: Zhang R, Manohar N, Arsoniadis E .
Evaluating term coverage of herbal and dietary supplements in electronic health records.
AMIA Annu Symp Proc 2015 Nov 5;2015:1361-70.
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Keywords: Adverse Drug Events (ADE), Data, Electronic Health Records (EHRs), Medication, Vitamins and Supplements
Price LE, Shea K, Gephart S
The Veterans Affairs's Corporate Data Warehouse: uses and implications for nursing research and practice.
This article described the developments in research associated with the VHA's transition into the world of Big Data analytics through Corporate Data Warehouse (CDW) utilization. The authors found that the most commonly-occurring research topics are pharmacy/medications, systems issues, and weight management/obesity. They concluded that, despite the potential benefit of data mining techniques to improve patient care and services, the CDW and alternative analytical approaches are underutilized by researchers and clinicians.
AHRQ-funded; HS022908.
Citation: Price LE, Shea K, Gephart S .
The Veterans Affairs's Corporate Data Warehouse: uses and implications for nursing research and practice.
Nurs Adm Q 2015 Oct-Dec;39(4):311-8. doi: 10.1097/naq.0000000000000118.
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Keywords: Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Nursing
Kim KK, Joseph JG, Ohno-Machado L
Comparison of consumers' views on electronic data sharing for healthcare and research.
The researchers surveyed California consumers to learn their views of privacy, security, and consent in electronic data sharing for healthcare and research together. They found considerable concern that health information exchanges will worsen privacy (40.3 percent) and security (42.5 percent). Consumers are in favor of electronic data sharing but elements of transparency are important: individual control, who has access, and the purpose for use of data.
AHRQ-funded; HS019913.
Citation: Kim KK, Joseph JG, Ohno-Machado L .
Comparison of consumers' views on electronic data sharing for healthcare and research.
J Am Med Inform Assoc 2015 Jul;22(4):821-30. doi: 10.1093/jamia/ocv014..
Keywords: Communication, Data, Electronic Health Records (EHRs), Health Information Exchange (HIE), Health Information Technology (HIT), Patient-Centered Healthcare