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Meaningful use 2: 2013's interoperability challenge. Connectivity barriers remain as physicians move from EHR implementation to data exchange, communication.

Medical economics

Bendix J.
PMID: 24066441
Med Econ. 2013 Mar 25;90(6):18-9, 24-7.

No abstract available.

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Bendix J. Meaningful use 2: 2013's interoperability challenge. Connectivity barriers remain as physicians move from EHR implementation to data exchange, communication. Med Econ. 2013;90(6):18-9, 24-7
Bendix, J. (2013). Meaningful use 2: 2013's interoperability challenge. Connectivity barriers remain as physicians move from EHR implementation to data exchange, communication. Medical economics, 90(6), 18-9, 24-7.
Bendix, Jeffrey. "Meaningful use 2: 2013's interoperability challenge. Connectivity barriers remain as physicians move from EHR implementation to data exchange, communication." Medical economics vol. 90,6 (2013): 18-9, 24-7.
Bendix J. Meaningful use 2: 2013's interoperability challenge. Connectivity barriers remain as physicians move from EHR implementation to data exchange, communication. Med Econ. 2013 Mar 25;90(6):18-9, 24-7. PMID: 24066441.
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Most provider organizations eyeing MU2 in 2014.

Medical economics

[No authors listed]
PMID: 24066460
Med Econ. 2013 Apr 10;90(7):70.

No abstract available.

Cite
Most provider organizations eyeing MU2 in 2014. Med Econ. 2013;90(7):70
(2013). Most provider organizations eyeing MU2 in 2014. Medical economics, 90(7), 70.
"Most provider organizations eyeing MU2 in 2014." Medical economics vol. 90,7 (2013): 70.
Most provider organizations eyeing MU2 in 2014. Med Econ. 2013 Apr 10;90(7):70. PMID: 24066460.
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Development of a tool to measure user experience following electronic health record implementation.

The Journal of nursing administration

Xiao Y, Montgomery DC, Philpot LM, Barnes SA, Compton J, Kennerly D.
PMID: 25072233
J Nurs Adm. 2014 Jul-Aug;44(7):423-8. doi: 10.1097/NNA.0000000000000093.

OBJECTIVE: The aim of this study was to develop a survey tool to assess electronic health record (EHR) implementation to guide improvement initiatives.BACKGROUND: Survey tools are needed for ongoing improvement and have not been developed for aspects of EHR...

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Xiao Y, Montgomery DC, Philpot LM, et al. Development of a tool to measure user experience following electronic health record implementation. J Nurs Adm. 2014;44(7):423-8doi: 10.1097/NNA.0000000000000093.
Xiao, Y., Montgomery, D. C., Philpot, L. M., Barnes, S. A., Compton, J., & Kennerly, D. (2014). Development of a tool to measure user experience following electronic health record implementation. The Journal of nursing administration, 44(7), 423-8. https://doi.org/10.1097/NNA.0000000000000093
Xiao, Yan, et al. "Development of a tool to measure user experience following electronic health record implementation." The Journal of nursing administration vol. 44,7 (2014): 423-8. doi: https://doi.org/10.1097/NNA.0000000000000093
Xiao Y, Montgomery DC, Philpot LM, Barnes SA, Compton J, Kennerly D. Development of a tool to measure user experience following electronic health record implementation. J Nurs Adm. 2014 Jul-Aug;44(7):423-8. doi: 10.1097/NNA.0000000000000093. PMID: 25072233.
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Electronic Health Records.

Annals of internal medicine

Hingle S.
PMID: 28384741
Ann Intern Med. 2017 Apr 04;166(7):536. doi: 10.7326/L17-0051.

No abstract available.

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Hingle S. Electronic Health Records. Ann Intern Med. 2017;166(7):536doi: 10.7326/L17-0051.
Hingle, S. (2017). Electronic Health Records. Annals of internal medicine, 166(7), 536. https://doi.org/10.7326/L17-0051
Hingle, Susan. "Electronic Health Records." Annals of internal medicine vol. 166,7 (2017): 536. doi: https://doi.org/10.7326/L17-0051
Hingle S. Electronic Health Records. Ann Intern Med. 2017 Apr 04;166(7):536. doi: 10.7326/L17-0051. PMID: 28384741.
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Computable Information Governance Contracts.

Studies in health technology and informatics

Cunningham J, Leeming G, Ainsworth J.
PMID: 28423838
Stud Health Technol Inform. 2017;235:476-480.

The risks of relinquishing control of electronic healthcare data for re-use in research are mitigated by the use of data sharing agreements and information governance procedures. These exist as legal, or quasi-legal, textual documents exchanged between data owners. Their...

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Cunningham J, Leeming G, Ainsworth J. Computable Information Governance Contracts. Stud Health Technol Inform. 2017;235:476-480
Cunningham, J., Leeming, G., & Ainsworth, J. (2017). Computable Information Governance Contracts. Studies in health technology and informatics, 235476-480.
Cunningham, James, et al. "Computable Information Governance Contracts." Studies in health technology and informatics vol. 235 (2017): 476-480.
Cunningham J, Leeming G, Ainsworth J. Computable Information Governance Contracts. Stud Health Technol Inform. 2017;235:476-480. PMID: 28423838.
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An Approach for the Support of Semantic Workflows in Electronic Health Records.

Studies in health technology and informatics

Schweitzer M, Hoerbst A.
PMID: 28423843
Stud Health Technol Inform. 2017;235:501-505.

With the unprecedented increase of healthcare data, technologies need to be both, highly efficient for the meaningful utilization of accessible data and flexible to adapt to future challenges and individual preferences. The OntoHealth system makes use of semantic technologies...

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Schweitzer M, Hoerbst A. An Approach for the Support of Semantic Workflows in Electronic Health Records. Stud Health Technol Inform. 2017;235:501-505
Schweitzer, M., & Hoerbst, A. (2017). An Approach for the Support of Semantic Workflows in Electronic Health Records. Studies in health technology and informatics, 235501-505.
Schweitzer, Marco, and Hoerbst, Alexander. "An Approach for the Support of Semantic Workflows in Electronic Health Records." Studies in health technology and informatics vol. 235 (2017): 501-505.
Schweitzer M, Hoerbst A. An Approach for the Support of Semantic Workflows in Electronic Health Records. Stud Health Technol Inform. 2017;235:501-505. PMID: 28423843.
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Making Electronic Health Records Work Better for Patient Care.

JAMA internal medicine

Ross JS.
PMID: 26974253
JAMA Intern Med. 2016 Apr;176(4):560. doi: 10.1001/jamainternmed.2016.0219.

No abstract available.

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Ross JS. Making Electronic Health Records Work Better for Patient Care. JAMA Intern Med. 2016;176(4):560doi: 10.1001/jamainternmed.2016.0219.
Ross, J. S. (2016). Making Electronic Health Records Work Better for Patient Care. JAMA internal medicine, 176(4), 560. https://doi.org/10.1001/jamainternmed.2016.0219
Ross, Joseph S. "Making Electronic Health Records Work Better for Patient Care." JAMA internal medicine vol. 176,4 (2016): 560. doi: https://doi.org/10.1001/jamainternmed.2016.0219
Ross JS. Making Electronic Health Records Work Better for Patient Care. JAMA Intern Med. 2016 Apr;176(4):560. doi: 10.1001/jamainternmed.2016.0219. PMID: 26974253.
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Can Social Cognitive Theories Help Us Understand Nurses' Use of Electronic Health Records?.

Computers, informatics, nursing : CIN

Strudwick G, Booth R, Mistry K.
PMID: 26844529
Comput Inform Nurs. 2016 Apr;34(4):169-74. doi: 10.1097/CIN.0000000000000226.

Electronic health record implementations have accelerated in clinical settings around the world in an effort to improve patient safety and enhance efficiencies related to care delivery. As the largest group of healthcare professionals globally, nurses play an important role...

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Strudwick G, Booth R, Mistry K. Can Social Cognitive Theories Help Us Understand Nurses' Use of Electronic Health Records?. Comput Inform Nurs. 2016;34(4):169-74doi: 10.1097/CIN.0000000000000226.
Strudwick, G., Booth, R., & Mistry, K. (2016). Can Social Cognitive Theories Help Us Understand Nurses' Use of Electronic Health Records?. Computers, informatics, nursing : CIN, 34(4), 169-74. https://doi.org/10.1097/CIN.0000000000000226
Strudwick, Gillian, et al. "Can Social Cognitive Theories Help Us Understand Nurses' Use of Electronic Health Records?." Computers, informatics, nursing : CIN vol. 34,4 (2016): 169-74. doi: https://doi.org/10.1097/CIN.0000000000000226
Strudwick G, Booth R, Mistry K. Can Social Cognitive Theories Help Us Understand Nurses' Use of Electronic Health Records?. Comput Inform Nurs. 2016 Apr;34(4):169-74. doi: 10.1097/CIN.0000000000000226. PMID: 26844529.
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Modeling Flowsheet Data to Support Secondary Use.

Computers, informatics, nursing : CIN

Westra BL, Christie B, Johnson SG, Pruinelli L, LaFlamme A, Sherman SG, Park JI, Delaney CW, Gao G, Speedie S.
PMID: 28346243
Comput Inform Nurs. 2017 Sep;35(9):452-458. doi: 10.1097/CIN.0000000000000350.

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same...

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Westra BL, Christie B, Johnson SG, et al. Modeling Flowsheet Data to Support Secondary Use. Comput Inform Nurs. 2017;35(9):452-458doi: 10.1097/CIN.0000000000000350.
Westra, B. L., Christie, B., Johnson, S. G., Pruinelli, L., LaFlamme, A., Sherman, S. G., Park, J. I., Delaney, C. W., Gao, G., & Speedie, S. (2017). Modeling Flowsheet Data to Support Secondary Use. Computers, informatics, nursing : CIN, 35(9), 452-458. https://doi.org/10.1097/CIN.0000000000000350
Westra, Bonnie L, et al. "Modeling Flowsheet Data to Support Secondary Use." Computers, informatics, nursing : CIN vol. 35,9 (2017): 452-458. doi: https://doi.org/10.1097/CIN.0000000000000350
Westra BL, Christie B, Johnson SG, Pruinelli L, LaFlamme A, Sherman SG, Park JI, Delaney CW, Gao G, Speedie S. Modeling Flowsheet Data to Support Secondary Use. Comput Inform Nurs. 2017 Sep;35(9):452-458. doi: 10.1097/CIN.0000000000000350. PMID: 28346243; PMCID: PMC5591037.
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A unified framework for evaluating the risk of re-identification of text de-identification tools.

Journal of biomedical informatics

Scaiano M, Middleton G, Arbuckle L, Kolhatkar V, Peyton L, Dowling M, Gipson DS, El Emam K.
PMID: 27426236
J Biomed Inform. 2016 Oct;63:174-183. doi: 10.1016/j.jbi.2016.07.015. Epub 2016 Jul 15.

OBJECTIVES: It has become regular practice to de-identify unstructured medical text for use in research using automatic methods, the goal of which is to remove patient identifying information to minimize re-identification risk. The metrics commonly used to determine if...

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Scaiano M, Middleton G, Arbuckle L, et al. A unified framework for evaluating the risk of re-identification of text de-identification tools. J Biomed Inform. 2016;63:174-183doi: 10.1016/j.jbi.2016.07.015.
Scaiano, M., Middleton, G., Arbuckle, L., Kolhatkar, V., Peyton, L., Dowling, M., Gipson, D. S., & El Emam, K. (2016). A unified framework for evaluating the risk of re-identification of text de-identification tools. Journal of biomedical informatics, 63174-183. https://doi.org/10.1016/j.jbi.2016.07.015
Scaiano, Martin, et al. "A unified framework for evaluating the risk of re-identification of text de-identification tools." Journal of biomedical informatics vol. 63 (2016): 174-183. doi: https://doi.org/10.1016/j.jbi.2016.07.015
Scaiano M, Middleton G, Arbuckle L, Kolhatkar V, Peyton L, Dowling M, Gipson DS, El Emam K. A unified framework for evaluating the risk of re-identification of text de-identification tools. J Biomed Inform. 2016 Oct;63:174-183. doi: 10.1016/j.jbi.2016.07.015. Epub 2016 Jul 15. PMID: 27426236.
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Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database.

Studies in health technology and informatics

Nienhoff H, Huebner U, Frey A, Przysucha M, Kimura M.
PMID: 26262371
Stud Health Technol Inform. 2015;216:1072.

The aim of this study was to investigate whether standard Big Data mining methods lead to clinically useful results. An association analysis was performed using the apriori algorithm to discover associations among co-morbidities of diabetes patients. Selected data were...

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Nienhoff H, Huebner U, Frey A, et al. Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Stud Health Technol Inform. 2015;216:1072
Nienhoff, H., Huebner, U., Frey, A., Przysucha, M., & Kimura, M. (2015). Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Studies in health technology and informatics, 2161072.
Nienhoff, Hendrik, et al. "Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database." Studies in health technology and informatics vol. 216 (2015): 1072.
Nienhoff H, Huebner U, Frey A, Przysucha M, Kimura M. Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Stud Health Technol Inform. 2015;216:1072. PMID: 26262371.
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Learning About Machine Learning: The Promise and Pitfalls of Big Data and the Electronic Health Record.

Circulation. Cardiovascular quality and outcomes

Deo RC, Nallamothu BK.
PMID: 28263936
Circ Cardiovasc Qual Outcomes. 2016 Nov;9(6):618-620. doi: 10.1161/CIRCOUTCOMES.116.003308. Epub 2016 Nov 08.

No abstract available.

Cite
Deo RC, Nallamothu BK. Learning About Machine Learning: The Promise and Pitfalls of Big Data and the Electronic Health Record. Circ Cardiovasc Qual Outcomes. 2016;9(6):618-620doi: 10.1161/CIRCOUTCOMES.116.003308.
Deo, R. C., & Nallamothu, B. K. (2016). Learning About Machine Learning: The Promise and Pitfalls of Big Data and the Electronic Health Record. Circulation. Cardiovascular quality and outcomes, 9(6), 618-620. https://doi.org/10.1161/CIRCOUTCOMES.116.003308
Deo, Rahul C, and Nallamothu, Brahmajee K. "Learning About Machine Learning: The Promise and Pitfalls of Big Data and the Electronic Health Record." Circulation. Cardiovascular quality and outcomes vol. 9,6 (2016): 618-620. doi: https://doi.org/10.1161/CIRCOUTCOMES.116.003308
Deo RC, Nallamothu BK. Learning About Machine Learning: The Promise and Pitfalls of Big Data and the Electronic Health Record. Circ Cardiovasc Qual Outcomes. 2016 Nov;9(6):618-620. doi: 10.1161/CIRCOUTCOMES.116.003308. Epub 2016 Nov 08. PMID: 28263936; PMCID: PMC5832331.
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Showing 1 to 12 of 1793 entries