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Journal articles on the topic 'Data'

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Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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1

Migdał-Najman, Kamila, and Krzysztof Najman. "BIG DATA = CLEAR + DIRTY + DARK DATA." Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, no. 469 (2017): 131–39. http://dx.doi.org/10.15611/pn.2017.469.13.

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2

Rakholiya, Kalpesh R., and Dr Dhaval Kathiriya. "Data Mining for Moving Object Data." Indian Journal of Applied Research 2, no. 3 (October 1, 2011): 111–13. http://dx.doi.org/10.15373/2249555x/dec2012/34.

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3

Arputhamary, B., and L. Arockiam. "Data Integration in Big Data Environment." Bonfring International Journal of Data Mining 5, no. 1 (February 10, 2015): 01–05. http://dx.doi.org/10.9756/bijdm.8001.

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4

Chomboon, K., N. Kaoungku, K. Kerdprasop, and N. Kerdprasop. "Data Mining in Semantic Web Data." International Journal of Computer Theory and Engineering 6, no. 6 (December 2014): 472–75. http://dx.doi.org/10.7763/ijcte.2014.v6.912.

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5

Zvyagin, L. S. "DATA MINING: BIG DATA AND DATA SCIENCE." SOFT MEASUREMENTS AND COMPUTING 5, no. 54 (2022): 81–90. http://dx.doi.org/10.36871/2618-9976.2022.05.006.

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Data mining is the process of discovering information that can be used in large amounts of data. This method uses mathematical analysis, which helps to identify patterns and trends in the data. Such patterns cannot be noticed during normal data viewing due to the complexity of the relationships that arise with a large amount of data. All of them are a set of tools and methods that help humanity in the changing world around us. It is becoming more and more voluminous, we receive huge aggregates of data on various processes. Big Data and Data Science allow large companies to systematize information about the markets in which they operate, which allows them to get a large amount of profit and benefits.

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6

Gültepe, Yasemin. "Querying Bibliography Data Based on Linked Data." Journal of Software 10, no. 8 (August 2015): 1014–20. http://dx.doi.org/10.17706//jsw.10.8.1014-1020.

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7

Sharma, Mansi, Palak Mittal, Nidhi Garg, and Prateek Jain. "Data Analysis FIFA World Cup Data Set." Indian Journal of Science and Technology 12, no. 39 (October 20, 2019): 1–4. http://dx.doi.org/10.17485/ijst/2019/v12i39/145565.

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8

Yerbulatov, Sultan. "Data Security and Privacy in Data Engineering." International Journal of Science and Research (IJSR) 13, no. 4 (April 5, 2024): 232–36. http://dx.doi.org/10.21275/es24318121241.

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9

Remize, Michel. "La data pour dada." Archimag N°310, no. 10 (December 1, 2017): 1. http://dx.doi.org/10.3917/arma.310.0001.

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10

Vala, Mr Manish, Kajal Patel, and Harsh Lad. "Multi Model Biometrics Data Retrieval Through: Big-Data." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 1273–77. http://dx.doi.org/10.31142/ijtsrd15933.

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11

S, Monisha, and Dr S. Venkateshkumar. "Cloud Computing in Data Backup and Data Recovery." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 865–67. http://dx.doi.org/10.31142/ijtsrd18652.

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12

Lenk, Peter, Michael Street, Ivana Ilic Mestric, Arvid Kok, Giavid Valiyev, Philippe Le Cerf, and Barbara Lorincz. "Data Science as a Service: The Data Range." Information & Security: An International Journal 47, no. 2 (2020): 157–71. http://dx.doi.org/10.11610/isij.4711.

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13

Shastri, Shankarayya, M. Pooja, and R. M. Soumyashree. "Data Analytics for Linked Data in Real Time." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 32–36. http://dx.doi.org/10.9756/bijsesc.8238.

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14

Horak, Karel. "Posun data." Zpravodaj Československého sdružení uživatelů TeXu 5, no. 1-4 (1995): 103–4. http://dx.doi.org/10.5300/1995-1-4/103.

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15

Gopinadh, J. "Data Outsourcing with Secure Data Auditing in Cloud Computing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 322–24. http://dx.doi.org/10.31142/ijtsrd12949.

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16

Kodabagi, M. M., Savita Rathod, and Vilas Naik. "Distributed Data Storage Technique for Big Data using Hadoop." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 43–48. http://dx.doi.org/10.9756/bijsesc.8240.

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17

Darshini, T. Divya, and S. Kalaiselvi. "Data-Driven Dynamic Fashion Market Insights Using Data Analytics." International Journal of Research Publication and Reviews 5, no. 3 (March 21, 2024): 4748–57. http://dx.doi.org/10.55248/gengpi.5.0324.0808.

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18

Scaife, Anna M. M., and Sally E. Cooper. "The DARA Big Data Project." Proceedings of the International Astronomical Union 14, A30 (August 2018): 569. http://dx.doi.org/10.1017/s174392131900543x.

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AbstractThe DARA Big Data project is a flagship UK Newton Fund & GCRF program in partnership with the South African Department of Science & Technology (DST). DARA Big Data provides bursaries for students from the partner countries of the African VLBI Network (AVN), namely Botswana, Ghana, Kenya, Madagascar, Mauritius, Mozambique, Namibia and Zambia, to study for MSc(R) and PhD degrees at universities in South Africa and the UK. These degrees are in the three data intensive DARA Big Data focus areas of astrophysics, health data and sustainable agriculture. The project also provides training courses in machine learning, big data techniques and data intensive methodologies as part of the Big Data Africa initiative.

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19

Blauth, Christopher. "Data, data, data…" International Journal of Clinical Practice 61, no. 7 (June 15, 2007): 1074. http://dx.doi.org/10.1111/j.1742-1241.2007.01405.x.

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20

Wegner, Julie. "Data, data, data…" Journal of ExtraCorporeal Technology 50, no. 4 (December 2018): 215–16. http://dx.doi.org/10.1051/ject/201850215.

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21

Villarroel, José Domingo, Maria Merino Maestre, and Alvaro Antón. "Data." IKASTORRATZA.e-journal on Didactics, September 23, 2020. http://dx.doi.org/10.37261/data.

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22

"Data Processing through Data Warehouse and Data mining." International Journal of Modern Trends in Engineering & Research 4, no. 5 (May 8, 2017): 45–48. http://dx.doi.org/10.21884/ijmter.2017.4151.1ea3x.

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23

David, John. "Data Mining and Data Warehousing." SIJ Transactions on Computer Science Engineering & its Applications (CSEA), June 28, 2019, 17–19. http://dx.doi.org/10.9756/sijcsea/v7i3/07010010105.

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24

Suchitra, B. "Semi-Structured Data Structured Data Conversion Using Data Mining Methods." International Journal of Emerging Trends in Science and Technology 4, no. 10 (April 30, 2017). http://dx.doi.org/10.18535/ijetst/v4i10.15.

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25

"Data Mining from Heterogeneous Data Sources." International Journal of Science and Research (IJSR) 6, no. 1 (January 5, 2017): 2076–79. http://dx.doi.org/10.21275/art20164530.

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26

"Data Anonymization Approach for Data Privacy." International Journal of Science and Research (IJSR) 4, no. 12 (December 5, 2015): 1534–39. http://dx.doi.org/10.21275/v4i12.12121502.

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27

Gouse, S. Mahammed. "Function of big-data over data streams." International Journal Of Engineering And Computer Science, September 23, 2016. http://dx.doi.org/10.18535/ijecs/v5i9.46.

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28

Gardner, Lea, and Susan Wallace. "Data Snapshot: Nasogastric Tube Misplacements." Patient Safety, March 17, 2021, 79–83. http://dx.doi.org/10.33940/data/2021.3.8.

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Nasogastric and orogastric tubes, herein collectively referred to as nasogastric tubes (NGT), are inserted into a patient’s nasal or oral cavity to administer feedings or medications or remove stomach contents. Tube misplacement is a known complication that can occur during insertion. This NGT misplacement data snapshot provides updated information.

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29

"Data Mining." International Journal of Recent Trends in Engineering and Research 3, no. 3 (April 3, 2017): 330–33. http://dx.doi.org/10.23883/ijrter.2017.3085.fple0.

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30

Tuchkova, A. S., and P. P. Kondrasheva. "The term "data mining". Tasks solved by data mining methods." SCIENTIFIC DEVELOPMENT TRENDS AND EDUCATION, 2019. http://dx.doi.org/10.18411/lj-10-2019-26.

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31

"Managing the Data Effectively Using Object Relational Data Store." International Journal of Science and Research (IJSR) 6, no. 7 (July 5, 2017): 669–73. http://dx.doi.org/10.21275/art20174900.

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32

"Review on Data Leakage Detection & Data Prevention Techniques." International Journal of Science and Research (IJSR) 6, no. 7 (July 5, 2017): 2116–20. http://dx.doi.org/10.21275/art20175726.

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33

"BIG DATA IN EDUCATION DATA MINING AND LEARNING ANALYTICS." International Journal of Modern Trends in Engineering & Research 3, no. 11 (November 25, 2016): 84–88. http://dx.doi.org/10.21884/ijmter.2016.3126.gb2rq.

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34

"DRAW: A New Data-gRouping-AWare Data Placement Scheme for Data Intensive Applications with Interest Locality." International Journal of Recent Trends in Engineering and Research 4, no. 6 (June 30, 2018): 172–81. http://dx.doi.org/10.23883/ijrter.2018.4333.w8uli.

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35

Bien, Jennylyn B. "Contextualized and localized up-to-Date Data Driven Lesson Materials in Statistics and Probability." International Journal of Social Science and Human Research 6, no. 10 (October 5, 2023). http://dx.doi.org/10.47191/ijsshr/v6-i10-06.

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Statistics and Probability has always been one of the most challenging subjects introduced through a spiral approach in the implementation of K-12 Education Curriculum. A stigma that has been apparent long before the COVID-19 Pandemic. But has become more evident for both the learners and teachers during distance learning. The researcher therefore aims to provide an intervention that is applicable even during situations of mandatory implementation of distance learning modalities. Specifically for schools who have no access to online learning modalities. The researcher used Contextualized and Localized Up-to-Date Data Driven Lesson Materials in Statistics and Probability to Grade 11 GAS learners of San Antonio National High School (SANHS) for SY 2022-2023. The materials were developed by the researcher and includes data from the school. Two materials were used in teaching the following competencies: (a) Calculates the mean and the variance of a discrete random variable M11/12SP-IIIb-2; (b) Interprets the mean and the variance of a discrete random variable M11/12SP-IIIb-3; and (c) Solves problems involving mean and variance of probability distributions M11/12SP-IIIb-4. The study also includes presenting how these Contextualized and Localized Up-to-Date Data Driven Lesson Materials have affected the attitudes of the respondents towards Statistics & Probability using the Survey of Attitudes Toward Statistics – 36 (SATS-36) by Schau, 2003. The conduct of this action research therefore aimed to answer the following research questions: (1) What contextualized and localized up-to-date data driven lesson materials can be used in teaching Statistics & Probability for the following Most Essential Competencies: (a) Calculates the mean and the variance of a discrete random variable. M11/12SP-IIIb-2; (b) Interprets the mean and the variance of a discrete random variable. M11/12SP-IIIb-3; (c) Solves problems involving mean and variance of probability distributions. M11/12SP-IIIb-4. (2) What is the attitude towards Statistics & Probability of Grade-11 GAS students before and after using the developed lesson materials? (3) What are the effects of the developed lessons materials on the students’ academic performance on the chosen competencies? Results of the action research conducted from August – October are as follows: (1) two contextualized and localized up-to-date data driven lesson materials were developed in teaching the following competencies: (a) Calculates the mean and the variance of a discrete random variable M11/12SP-IIIb-2; (b) Interprets the mean and the variance of a discrete random variable M11/12SP-IIIb-3; and (c) Solves problems involving mean and variance of probability distributions M11/12SP-IIIb-4. (2) The Grade 11-GAS learners showed improvement towards their attitudes towards statistics and probability. Specifically in terms of affect, cognitive competence, difficulty, interest, and effort. (3) The developed contextualized and localized up- to-date data driven lesson materials have improved the academic performance of the Grade 11- GAS learners. Specifically, with reference to the administered teacher-made pre/post-test, the respondents showed increase in performance level to each of the three competencies. But such increases performance level was not enough for the respondents to achieve mastery level. Hence, the results suggests that there is a need to further improve the developed materials.

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36

Harper, Amy, Elizabeth Kukielka, and Rebecca Jones. "Patient Harm Resulting From Medication Reconciliation Process Failures: A Study of Serious Events Reported by Pennsylvania Hospitals." Patient Safety, February 26, 2021, 10–22. http://dx.doi.org/10.33940/data/2021.3.1.

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Medication reconciliation broadly defined includes both formal and informal processes that involve the comprehensive evaluation of a patient’s medications during each transition of care and change in therapy. The medication reconciliation process is complex, and studies have shown that up to 91% of medication reconciliation errors are clinically significant and 1–2% are serious or potentially life-threatening. We queried the Pennsylvania Patient Safety Reporting System (PA-PSRS) and identified 93 serious events related to the medication reconciliation process reported between January 2015 and August 2020. Serious events related to medication reconciliation were most common among patients 65 years or older (55.9%; 52 of 93). The majority of events (58.1%; 54 of 93) contributed to or resulted in temporary harm and required treatment or intervention. Permanent harm or death occurred as a result of 3.3% (3 of 93) of the events. Admission/triage was the most frequent transition of care associated with events (69.9%; 65 of 93). The most common stage of the medication reconciliation process at which failures most directly contributed to patient harm was order entry/transcription (41.9%; 39 of 93) and resulted most frequently in wrong dose (n=21) or dose omission (n=13). Most events were discovered after the patient had a change in condition (76.3%; 71 of 93), and patients most often required readmission, hospitalization, emergency care, intensive care, or transfer to a higher level of care (58.0%; 54 of 93). Among 128 medications identified across all events, neurologic or psychiatric medications were the most common (39.1%; 50 of 128), and anticonvulsants were the most common pharmacologic class among neurologic or psychiatric medications (42.0%; 21 of 50). Based on our findings, risk reduction strategies that may improve patient safety related to the medication reconciliation process include defined clinician roles for medication reconciliation, listing the indication for each medication prescribed, and for facilities to consider adding anticonvulsants to their processes for medications with a high risk for harm.

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37

Kim, Tracy, Jessica Howe, Ella Franklin, Seth Krevat, Rebecca Jones, Katharine Adams, Allan Fong, Jessica Oaks, and Raj Ratwani. "Health Information Technology–Related Wrong-Patient Errors: Context is Critical." Patient Safety, December 17, 2020, 40–57. http://dx.doi.org/10.33940/data/20.12.3.

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Health information technology (HIT) provides many benefits, but also facilitates certain types of errors, such as wrong-patient errors in which one patient is mistaken for another. These errors can have serious patient safety consequences and there has been significant effort to mitigate the risk of these errors through national patient safety goals, in-depth research, and the development of safety toolkits. Nonetheless, these errors persist. We analyzed 1,189 patient safety event reports using a safety science and resilience engineering approach, which focuses on identifying processes to discover errors before they reach the patient so these processes can be expanded. We analyzed the general care processes in which wrong-patient errors occurred, the clinical process step during which the error occurred and was discovered, and whether the error reached the patient. For those errors that reached the patient, we analyzed the impact on the patient, and for those that did not reach the patient, we analyzed how the error was caught. Our results demonstrate that errors occurred across multiple general care process areas, with 24.4% of wrong-patient error events reaching the patient. Analysis of clinical process steps indicated that most errors occurred during ordering/prescribing (n=498; 41.9%) and most errors were discovered during review of information (n=286; 24.1%). Patients were primarily impacted by inappropriate medication administration (n=110; 37.9%) and the wrong test or procedure being performed (n=65; 22.4%). When errors were caught before reaching the patient, this was primarily because of nurses, technicians, or other healthcare staff (n=303; 60.5%). The differences between the general care processes can inform wrong-patient error risk mitigation strategies. Based on these analyses and the broader literature, this study offers recommendations for addressing wrong-patient errors using safety science and resilience engineering, and it provides a unique lens for evaluating HIT wrong-patient errors.

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38

Hoffman, Regina. "How to Interpret Patient Safety Data—A Guide From the Nation's Largest Event Reporting Database." Patient Safety, September 17, 2019, 8–9. http://dx.doi.org/10.33940/data/2019.9.1.

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39

Liberatore, Kim, and Barry Kohler. "Batteries Gone Bad." Patient Safety, September 17, 2019, 58–59. http://dx.doi.org/10.33940/data/2019.9.8.

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40

Gardner, Lea. "Prone Positioning in Patients With Acute Respiratory Distress Syndrome and Other Respiratory Conditions: Challenges, Complications, and Solutions." Patient Safety, December 17, 2020, 11–23. http://dx.doi.org/10.33940/data/2020.12.1.

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Acute respiratory distress syndrome (ARDS) and respiratory failure are characterized by hypoxemia, i.e., low levels of blood oxygen. Infections such as influenza and COVID-19 can lead to ARDS or respiratory failure. Treatment is through supportive measures. In severe cases, patients receive oxygen through a ventilator and, when appropriate, are placed in a prone position for an extended period. A retrospective review of events submitted to the Pennsylvania Patient Safety Reporting System (PA-PSRS) identified 98 prone position–related events in patients with ARDS, respiratory failure, distress, and pneumonia from January 1, 2010, through June 30, 2020; 30 events were associated with COVID-19. Skin integrity injuries accounted for 83.7% (82 of 98) of the events. The remaining events, 16.3% (16 of 98), involved unplanned extubations, cardiac arrests, displaced lines, enteral feedings, medication errors, a dental issue, and posterior ischemic optic neuropathy.

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41

Yonash, Robert, and Matthew Taylor. "Wrong-Site Surgery in Pennsylvania During 2015–2019: A Study of Variables Associated With 368 Events From 178 Facilities." Patient Safety, December 17, 2020, 24–39. http://dx.doi.org/10.33940/data/2020.12.2.

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Wrong-site surgery (WSS) is a well-known type of medical error that may cause a high degree of patient harm. In Pennsylvania, healthcare facilities are mandated to report WSS events, among other patient safety concerns, to the Pennsylvania Patient Safety Reporting System (PA-PSRS) database. In the study we identified instances of WSS events (not including near misses) that occurred during 2015–2019 and were reported to PA-PSRS. During the five-year period, we found that 178 healthcare facilities reported a total of 368 WSS events, which was an average of 1.42 WSS events per week in Pennsylvania. Also, we revealed that 76% (278 of 368) of the WSS events contributed to or resulted in temporary harm or permanent harm to the patient. Overall, the study shows that the frequency of WSS varied according to a range of variables, including error type (e.g., wrong side, wrong site, wrong procedure, wrong patient); year; facility type; hospital bed size; hospital procedure location; procedure; body region; body part; and clinician specialty. Our findings are aligned with some of the previous research on WSS; however, the current study also addresses many gaps in the literature. We encourage readers to use the visuals in the manuscript and appendices to gain new insight into the relation among the variables associated with WSS. Ultimately, the findings reported in the current study help to convey a more complete account of the variables associated with WSS, which can be used to assist staff in making informed decisions about allocating resources to mitigate risk.

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42

Kukielka, Elizabeth. "An Analysis of Patient Safety Events Submitted by Abortion Facilities in Pennsylvania 2017–2019." Patient Safety, December 17, 2020, 62–71. http://dx.doi.org/10.33940/data/2020.12.5.

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Induced abortion, also called elective abortion, therapeutic abortion, and termination of pregnancy, is widely considered a safe procedure, but complications are known to occur. In Pennsylvania, an induced abortion may be performed at an abortion facility as an outpatient procedure, and these facilities are required to report patient safety events to the Pennsylvania Patient Safety Reporting System (PA-PSRS). We extracted 736 events submitted to PAPSRS by abortion facilities from 2017 through 2019 and analyzed these events in order to better understand patient safety concerns at abortion facilities in particular. All patients were female, and they ranged in age from 14 to 47 years, with a median patient age of 27 years (interquartile range = 23 to 31 years). Complications related to an induced abortion comprised the majority of events (71.6%; n=527), followed by unplanned transfers to the emergency department or acute visits to a healthcare facility following an induced abortion (13.9%; n=102). The most common complication associated with induced abortion was an incomplete abortion (i.e., retained pregnancy tissue; n=343); other complications included failed abortions (i.e., a continuing intrauterine pregnancy following an abortion; n=101), infections (e.g., endometritis and pelvic inflammatory disease [PID]; n=45), and surgical complications (e.g., hematometra, uterine perforation, and cervical lacerations; n=66). The remainder of events (14.5%; n=107) described other patient safety events that occurred at abortion facilities, such as documentation failures and medication-related events.

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43

Krukas, Adam, Ella Franklin, Chris Bonk, Jessica Howe, Ram Dixit, Katie Adams, Seth Krevat, Rebecca Jones, and Raj Ratwani. "Identifying Safety Hazards Associated With Intravenous Vancomycin Through the Analysis of Patient Safety Event Reports." Patient Safety, March 17, 2020, 31–47. http://dx.doi.org/10.33940/data/2020.3.3.

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Intravenous (IV) vancomycin is one of the most commonly used antibiotics in U.S. hospitals. There are several complexities associated with IV vancomycin use, including the need to have an accurate patient weight for dosing, to provide close monitoring to ensure appropriate drug levels, to monitor renal function, and to continue delivery of the medication at prescribed intervals. There are numerous healthcare system factors, including workflow processes, policies, health information technology, and clinical knowledge that impact the safe use of IV vancomycin. Past literature has identified several safety hazards associated with IV vancomycin use and there are some proposed solutions. Despite this literature, IV vancomycin–related safety issues persist. We analyzed patient safety event reports describing IV vancomycin–related issues in order to identify where in the medication process these issues were appearing, the type of medication error associated with each report, and general contributing factor themes. Our results demonstrate that recent safety reports are aligned with the issues already identified in the literature, suggesting that improvements discussed in the literature have not translated to clinical practice. Based on our analysis and current literature, we have developed a shareable infographic to improve clinician awareness of the complications and safety hazards associated with IV vancomycin and a self-assessment tool to support identification of opportunities to improve patient safety during IV vancomycin therapy. We also recommend development of clear guidelines to optimize health information technology systems to better support safe IV vancomycin use.

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44

Kukielka, Eliabeth. "How Safety Is Compromised When Hospital Equipment Is a Poor Fit for Patients Who Are Obese." Patient Safety, March 17, 2020, 48–56. http://dx.doi.org/10.33940/data/2020.3.4.

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Obesity is common, serious, and costly, and according to recent data, its prevalence is on the rise in the United States. Event reports submitted to the Pennsylvania Patient Safety Reporting System (PA-PSRS) indicate that some healthcare facilities do not have the necessary equipment to monitor and care for some individuals in this patient population, leading to embarrassment for patients, delays in care, and injuries to patients. An analysis of 107 events related to monitoring and patient care for patients who are obese submitted to PA-PSRS from 2009 through 2018 showed that imaging equipment, especially MRI and CT scanners, was most often implicated in event reports (49.5%; 53 events); other equipment included stretchers (24.3%; 26 events) and wheelchairs (11.2%; 12 events). Events most often occurred in an imaging department (30.8%; 33 events) or a medical/surgical unit (21.5%; 23 events). Analysts determined that 80 events (74.8%) resulted in a delay in care and that 44 events (41.1%) resulted in temporary harm to the patient, including skin tears and abrasions. Healthcare providers may not be able to prevent delays in care resulting from the unavailability of adequate equipment for patients who are obese, but they may be able to prevent harm and embarrassment for patients through proactive assessment.

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45

Kepner, Shawn, Rebecca Jones, Caitlyn Allen, Daniel Glunk, Eric Weitz, and Stanton Smullens. "2019 Pennsylvania Patient Safety Reporting: An Analysis of Serious Events and Incidents From the Nation's Largest Event Reporting Database." Patient Safety, April 30, 2020. http://dx.doi.org/10.33940/data/2020.6.1.

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Pennsylvania is the only state that requires healthcare facilities to report all events of harm or potential for harm. Serious Events and Incidents are reported to the Pennsylvania Patient Safety Reporting System (PA-PSRS)*, which is the largest repository of patient safety data in the United States, and one of the largest in the world, with over 3.6 million acute care records. The overwhelming majority (97.1%) of all acute care event reports are Incidents. For 2019, there were 284,847 Incidents and 8,553 Serious Events for a total of 293,400 reported events. The counts of all events and the percentage that are Serious Events reported over the last eight years are provided in Figure 1. The total number of event reports has increased during the last four years. The number of reported Serious Events has increased over the past three years with the largest annual increase occurring in 2019 (+5.7%). This article will show details of the PA-PSRS acute care data along with longitudinal and categorical insights that can be used for improving patient safety.

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46

Kepner, Shawn, Amy Harper, Rebecca Jones, Caitlyn Allen, Regina Hoffman, Daniel Glunk, Eric Weitz, and Stanton Smullens. "Healthcare-Associated Infections in the Long-Term Care Setting: An Analysis of Reports From Pennsylvania." Patient Safety, April 30, 2020. http://dx.doi.org/10.33940/data/2020.6.2.

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The Pennsylvania Patient Safety Reporting System (PA-PSRS) is the largest repository of patient safety data in the United States. In addition to over 3.6 million Acute Care records, PA-PSRS has collected more than 330,000 Long-Term Care (LTC) healthcare-associated in¬fection reports since 2009. A total of 28,310 infections were reported in 2019, representing a 9% decrease from the prior year. The Northwest region of the state had the highest infection reporting rate, with 1.25 reports per 1,000 resident days. There was a 20% reduction in both the number and reporting rate of respiratory tract infections from 2018 to 2019; however, respiratory tract infections remained the most frequently reported infection type overall. Cellulitis, soft tissue, or wound infection was the most frequently reported infection subtype in 2019, followed by pneumonia and symptomatic urinary tract infection. With this information, nursing homes and interested parties can determine which trends or characteristics of the data are relevant for reduction in infections in nursing homes. Overall, this analysis demonstrates areas in which continued education and infection prevention measures can be applied to further enhance the safety for residents in long-term care facilities.

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Taylor, Matthew, Shawn Kepner, Lea Anne Gardner, and Rebecca Jones. "Patient Safety Concerns in COVID-19–Related Events: A Study of 343 Event Reports From 71 Hospitals in Pennsylvania." Patient Safety, June 17, 2020, 16–27. http://dx.doi.org/10.33940/data/2020.6.3.

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COVID-19 (i.e., coronavirus disease 2019) was declared a pandemic and has had a profound impact on healthcare systems, which may increase the risk of patient harm. We conducted a query of the Pennsylvania Patient Safety Reporting System (PA-PSRS) database to identify COVID-19–related events submitted by acute care hospitals between January 1 and April 15, 2020. We identified 343 relevant event reports from 71 hospitals and conducted a descriptive study to identify the prevalence of and relationships between 13 categories of associated factors and 6 categories of event outcomes. We found that 36% (124 of 343) of events had more than one associated factor and 24% (83 of 343) had more than one outcome. The most frequently identified factors were Laboratory Testing (47%; 161 of 343), Process/Protocol (25%; 87 of 343), and Isolation Integrity (22%; 74 of 343). The two most frequent outcomes were Exposure to COVID-19 Positive or Suspected Positive Patient (50%; 173 of 343) and Missed/Delayed Test or Result (31%; 108 of 343). Finally, the findings showed that seven of the associated factors had a notable impact on the frequency of Exposure to COVID-19 Positive or Suspected Positive Patient outcome. Overall, we anticipate that the results can be used to identify areas of greatest need and risk, which could help to guide allocation of resources to mitigate risk of patient harm.

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Taylor, Matthew, Alex Nowalk, and Alex Falk. "In-Hospital Substance Use and Possession: A Study of Events From 38 Acute Care Hospitals in Pennsylvania." Patient Safety, June 17, 2020, 56–68. http://dx.doi.org/10.33940/data/2020.6.6.

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Patients’ substance use and possession at acute care hospitals is an understudied topic. To learn more about this topic, we queried the Pennsylvania Patient Safety Reporting System (PA-PSRS) for events that occurred during calendar year 2018. We identified 106 reports from 38 acute care hospitals (excluding psychiatric, detox, and behavioral health units and facilities) where a patient possessed and/or misused a substance (e.g., heroin, oxycodone, liquor). We analyzed these reports to better understand how hospital personnel attempt to prevent in-hospital substance use and manage patients who are at risk for using a substance. We explored a range of variables, including antecedent conditions and hospital personnel’s actions post- detection of a patient’s substance use or possession. We found that a relatively low percentage of patients (26%) were identified as having a prior history of substance use, despite later using or being in possession of a substance in hospital. In our sample, patients frequently acquired the substances from visitors, more than half of the substances were consumed intravenously, and opioids were the most common substance. The most prevalent actions taken by hospital personnel were conducting searches for substances and paraphernalia, use of a patient sitter or video monitor, moving patients to a different room, and implementing visitor restrictions. Based on our findings and previous research, hospitals should consider increasing their use of substance use disorder (SUD) screening tools, pharmacotherapy, and referring patients to treatment. Overall, our results can help personnel better understand the nature of and strategies that may reduce the likelihood of in-hospital substance use. Keywords: substance use, paraphernalia, substance use disorder, in-hospital, addiction, opioid, risk mitigation, patient safety

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Kukielka, Elizabeth, and Rebecca Jones. "Swallowed Dentures: An Analysis of Denture Impaction Events in Pennsylvania." Patient Safety, June 17, 2020, 69–79. http://dx.doi.org/10.33940/data/2020.6.7.

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Tooth loss is a prevalent health concern affecting two-thirds of the geriatric population in the United States. Most patients replace missing teeth with dentures, which have the potential to become dislodged, swallowed, and stuck somewhere along the gastrointestinal tract (termed denture impaction). We queried the Pennsylvania Patient Safety Reporting System (PA-PSRS) and identified 68 denture impaction events reported from 2004 to 2019. An in-depth analysis revealed that patients were most often male, and the median patient age was 77 years. The most common symptoms reported by patients with denture impaction included difficult and/or painful swallowing, breathing trouble or respiratory distress, sore throat, foreign body sensation in the throat, throat obstruction preventing insertion of a tube or scope, choking, excessively thick and/or bloody oral secretions, and vomiting or regurgitation of food. The pharynx was the most common site of denture impaction, and x-ray imaging was the most common diagnostic test mentioned. The most common method of removal was surgery, and the most common surgical procedures employed were esophagogastroduodenoscopy and laryngoscopy. We believe that we have identified a category of denture impaction events that has not previously been characterized. Our study, coupled with the existing medical literature, suggests that all patients, along with their caregivers and healthcare providers, should be aware of the proactive steps to avoid denture impaction, as well as signs and symptoms of impaction for early identification and treatment. Keywords: choking, denture, denture impaction, edentulism, intubation, swallowed dentures, delayed diagnosis

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Harper, Amy, Elizabeth Kukielka, and Rebecca Jones. "Process Failures That Increase the Risk of Infection Through Respiratory Droplets: A Study of Patient Safety Events Reported by Hospitals Across Pennsylvania." Patient Safety, September 17, 2020, 10–23. http://dx.doi.org/10.33940/data/2020.9.1.

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Respiratory pathogens can lead to pneumonia, bronchiolitis, and death. Rapid identification, along with appropriate standard and isolation precautions, are necessary to prevent the spread of infectious agents causing respiratory infections. We analyzed patient safety events reported to the Pennsylvania Patient Safety Reporting System that were related to viruses and bacteria spread through respiratory droplets. An analysis of events that occurred from January 1, 2019, through December 31, 2019, led to the identification of 338 events involving process failures related to recognizing infectious agents that are spread through respiratory droplets, implementing measures to prevent their spread, or providing timely treatment. Detailed analysis of the process failures showed that 54.9% were associated with processes in testing or processing of laboratory specimens; 29.7% were associated with isolation-related procedures; and 15.4% were associated with medications, triage/assessment, documentation/verbal communication, or not providing the standard of care for patients in missed/delayed orders, procedures, or referrals. Implementation of risk-reduction strategies can help to further reduce the spread of pathogens through respiratory droplets in the hospital setting and further enhance patient safety. These strategies include evaluating collection processes for testing/laboratory specimens, consistently using empiric isolation precautions based on initial triage and patient presentation, and evaluating processes for admissions and transfers.

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