An introduction to healthcare informatics: building data-driven tools
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
London, United Kingdom
Academic Press, an imprint of Elsevier
[2020]
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| Subjects: | |
| Online Access: | DE-1050 DE-188 DE-91 Volltext |
| Physical Description: | 1 Online-Ressource (xvi, 323 Seiten) |
| ISBN: | 9780128149164 |
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| 505 | 8 | |a Intro -- An Introduction to Healthcare Informatics: Building Data-Driven Tools -- Copyright -- Dedication -- Contents -- Author's biography -- Foreword -- Section 1: Storing and accessing data -- Chapter 1: The healthcare IT landscape -- 1.1. How we got here and the growth of healthcare IT -- 1.2. The role of informatics -- 1.3. Common architectural aspects of healthcare IT -- 1.4. Device and application levels -- 1.5. Communication level -- 1.6. Process level -- 1.7. Common organizational aspects of healthcare IT -- 1.8. Physician and nurse informaticists | |
| 505 | 8 | |a 1.9. Regulatory aspects of healthcare IT -- 1.10. Challenges and opportunities -- 1.11. Conclusion -- References -- Chapter 2: Relational databases -- 2.1. A brief history of SQL and relational databases -- 2.2. Overview of the relational model -- 2.3. Differences between the relational model and SQL -- 2.4. Primary and foreign keys -- 2.5. ACID and transactions with data -- 2.6. Normalization -- 2.7. Conclusion -- References -- Chapter 3: SQL -- 3.1. Getting started with SQL -- 3.2. Structure of SQL databases -- 3.3. Basic SQL: SELECT, FROM, WHERE, and ORDER BY statements | |
| 505 | 8 | |a 3.4. Basic SQL: GROUP BY and general aggregate functions -- 3.5. Intermediate SQL: Joins -- 3.6. Advanced SQL: Window functions -- 3.7. SQL concept: Indexes -- 3.8. SQL concept: Schemas -- 3.9. Advanced SQL: SubQueries -- 3.10. Conclusion -- Chapter 4: Example project 1: Querying data with SQL -- 4.1. Introduction and project background -- 4.2. Viewing tables -- 4.3. Querying tables -- 4.3.1. Average number of visits per day per location -- 4.3.2. Average patient age and patient sex per location -- 4.3.3. Average number of patient visits per provider | |
| 505 | 8 | |a 4.3.4. Counts of diagnosis codes and average age per clinic location -- 4.4. Conclusion -- Chapter 5: Nonrelational databases -- 5.1. Early nonrelational models -- 5.2. The rise of modern nonrelational models -- 5.3. Key-value stores -- 5.4. Document stores -- 5.5. Column stores -- 5.5.1. Traditional column stores -- 5.5.2. Wide column stores -- 5.6. Graph databases -- 5.7. Conclusion -- Reference -- Chapter 6: M/MUMPS -- 6.1. A brief history and context -- 6.2. The M language -- 6.3. General concepts regarding arrays and MUMPS -- 6.4. Arrays and MUMPS | |
| 505 | 8 | |a 6.5. MUMPS, globals, and data infrastructure -- 6.6. Conclusion -- References -- Section 2: Understanding healthcare data -- Chapter 7: How to approach healthcare data questions -- 7.1. Introduction -- 7.2. Healthcare as a CAS -- 7.3. Drivers of fallacy: Chance and bias -- 7.4. Missingness -- 7.5. Selecting tractable areas for intervention -- 7.6. Data and trust -- 7.7. Conclusion -- Chapter 8: Clinical and administrative workflows: Encounters, laboratory testing, clinical notes, and billing -- 8.1. Introduction -- 8.2. Encounters, patients, and episodes of care | |
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| contents | Intro -- An Introduction to Healthcare Informatics: Building Data-Driven Tools -- Copyright -- Dedication -- Contents -- Author's biography -- Foreword -- Section 1: Storing and accessing data -- Chapter 1: The healthcare IT landscape -- 1.1. How we got here and the growth of healthcare IT -- 1.2. The role of informatics -- 1.3. Common architectural aspects of healthcare IT -- 1.4. Device and application levels -- 1.5. Communication level -- 1.6. Process level -- 1.7. Common organizational aspects of healthcare IT -- 1.8. Physician and nurse informaticists 1.9. Regulatory aspects of healthcare IT -- 1.10. Challenges and opportunities -- 1.11. Conclusion -- References -- Chapter 2: Relational databases -- 2.1. A brief history of SQL and relational databases -- 2.2. Overview of the relational model -- 2.3. Differences between the relational model and SQL -- 2.4. Primary and foreign keys -- 2.5. ACID and transactions with data -- 2.6. Normalization -- 2.7. Conclusion -- References -- Chapter 3: SQL -- 3.1. Getting started with SQL -- 3.2. Structure of SQL databases -- 3.3. Basic SQL: SELECT, FROM, WHERE, and ORDER BY statements 3.4. Basic SQL: GROUP BY and general aggregate functions -- 3.5. Intermediate SQL: Joins -- 3.6. Advanced SQL: Window functions -- 3.7. SQL concept: Indexes -- 3.8. SQL concept: Schemas -- 3.9. Advanced SQL: SubQueries -- 3.10. Conclusion -- Chapter 4: Example project 1: Querying data with SQL -- 4.1. Introduction and project background -- 4.2. Viewing tables -- 4.3. Querying tables -- 4.3.1. Average number of visits per day per location -- 4.3.2. Average patient age and patient sex per location -- 4.3.3. Average number of patient visits per provider 4.3.4. Counts of diagnosis codes and average age per clinic location -- 4.4. Conclusion -- Chapter 5: Nonrelational databases -- 5.1. Early nonrelational models -- 5.2. The rise of modern nonrelational models -- 5.3. Key-value stores -- 5.4. Document stores -- 5.5. Column stores -- 5.5.1. Traditional column stores -- 5.5.2. Wide column stores -- 5.6. Graph databases -- 5.7. Conclusion -- Reference -- Chapter 6: M/MUMPS -- 6.1. A brief history and context -- 6.2. The M language -- 6.3. General concepts regarding arrays and MUMPS -- 6.4. Arrays and MUMPS 6.5. MUMPS, globals, and data infrastructure -- 6.6. Conclusion -- References -- Section 2: Understanding healthcare data -- Chapter 7: How to approach healthcare data questions -- 7.1. Introduction -- 7.2. Healthcare as a CAS -- 7.3. Drivers of fallacy: Chance and bias -- 7.4. Missingness -- 7.5. Selecting tractable areas for intervention -- 7.6. Data and trust -- 7.7. Conclusion -- Chapter 8: Clinical and administrative workflows: Encounters, laboratory testing, clinical notes, and billing -- 8.1. Introduction -- 8.2. Encounters, patients, and episodes of care |
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| spelling | McCaffrey, Peter ca. 20./21. Jh. Verfasser (DE-588)121735493X aut An introduction to healthcare informatics building data-driven tools Peter Mccaffrey London, United Kingdom Academic Press, an imprint of Elsevier [2020] © 2020 1 Online-Ressource (xvi, 323 Seiten) txt rdacontent c rdamedia cr rdacarrier Intro -- An Introduction to Healthcare Informatics: Building Data-Driven Tools -- Copyright -- Dedication -- Contents -- Author's biography -- Foreword -- Section 1: Storing and accessing data -- Chapter 1: The healthcare IT landscape -- 1.1. How we got here and the growth of healthcare IT -- 1.2. The role of informatics -- 1.3. Common architectural aspects of healthcare IT -- 1.4. Device and application levels -- 1.5. Communication level -- 1.6. Process level -- 1.7. Common organizational aspects of healthcare IT -- 1.8. Physician and nurse informaticists 1.9. Regulatory aspects of healthcare IT -- 1.10. Challenges and opportunities -- 1.11. Conclusion -- References -- Chapter 2: Relational databases -- 2.1. A brief history of SQL and relational databases -- 2.2. Overview of the relational model -- 2.3. Differences between the relational model and SQL -- 2.4. Primary and foreign keys -- 2.5. ACID and transactions with data -- 2.6. Normalization -- 2.7. Conclusion -- References -- Chapter 3: SQL -- 3.1. Getting started with SQL -- 3.2. Structure of SQL databases -- 3.3. Basic SQL: SELECT, FROM, WHERE, and ORDER BY statements 3.4. Basic SQL: GROUP BY and general aggregate functions -- 3.5. Intermediate SQL: Joins -- 3.6. Advanced SQL: Window functions -- 3.7. SQL concept: Indexes -- 3.8. SQL concept: Schemas -- 3.9. Advanced SQL: SubQueries -- 3.10. Conclusion -- Chapter 4: Example project 1: Querying data with SQL -- 4.1. Introduction and project background -- 4.2. Viewing tables -- 4.3. Querying tables -- 4.3.1. Average number of visits per day per location -- 4.3.2. Average patient age and patient sex per location -- 4.3.3. Average number of patient visits per provider 4.3.4. Counts of diagnosis codes and average age per clinic location -- 4.4. Conclusion -- Chapter 5: Nonrelational databases -- 5.1. Early nonrelational models -- 5.2. The rise of modern nonrelational models -- 5.3. Key-value stores -- 5.4. Document stores -- 5.5. Column stores -- 5.5.1. Traditional column stores -- 5.5.2. Wide column stores -- 5.6. Graph databases -- 5.7. Conclusion -- Reference -- Chapter 6: M/MUMPS -- 6.1. A brief history and context -- 6.2. The M language -- 6.3. General concepts regarding arrays and MUMPS -- 6.4. Arrays and MUMPS 6.5. MUMPS, globals, and data infrastructure -- 6.6. Conclusion -- References -- Section 2: Understanding healthcare data -- Chapter 7: How to approach healthcare data questions -- 7.1. Introduction -- 7.2. Healthcare as a CAS -- 7.3. Drivers of fallacy: Chance and bias -- 7.4. Missingness -- 7.5. Selecting tractable areas for intervention -- 7.6. Data and trust -- 7.7. Conclusion -- Chapter 8: Clinical and administrative workflows: Encounters, laboratory testing, clinical notes, and billing -- 8.1. Introduction -- 8.2. Encounters, patients, and episodes of care Medizinische Informatik (DE-588)4038261-8 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Medizinische Informatik (DE-588)4038261-8 s DE-604 Erscheint auch als Druck-Ausgabe 978-0-12-814915-7 Erscheint auch als Druck-Ausgabe 0128149159 Erscheint auch als Druck-Ausgabe 9780128149164 https://www.sciencedirect.com/science/book/9780128149157 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | McCaffrey, Peter ca. 20./21. Jh An introduction to healthcare informatics building data-driven tools Intro -- An Introduction to Healthcare Informatics: Building Data-Driven Tools -- Copyright -- Dedication -- Contents -- Author's biography -- Foreword -- Section 1: Storing and accessing data -- Chapter 1: The healthcare IT landscape -- 1.1. How we got here and the growth of healthcare IT -- 1.2. The role of informatics -- 1.3. Common architectural aspects of healthcare IT -- 1.4. Device and application levels -- 1.5. Communication level -- 1.6. Process level -- 1.7. Common organizational aspects of healthcare IT -- 1.8. Physician and nurse informaticists 1.9. Regulatory aspects of healthcare IT -- 1.10. Challenges and opportunities -- 1.11. Conclusion -- References -- Chapter 2: Relational databases -- 2.1. A brief history of SQL and relational databases -- 2.2. Overview of the relational model -- 2.3. Differences between the relational model and SQL -- 2.4. Primary and foreign keys -- 2.5. ACID and transactions with data -- 2.6. Normalization -- 2.7. Conclusion -- References -- Chapter 3: SQL -- 3.1. Getting started with SQL -- 3.2. Structure of SQL databases -- 3.3. Basic SQL: SELECT, FROM, WHERE, and ORDER BY statements 3.4. Basic SQL: GROUP BY and general aggregate functions -- 3.5. Intermediate SQL: Joins -- 3.6. Advanced SQL: Window functions -- 3.7. SQL concept: Indexes -- 3.8. SQL concept: Schemas -- 3.9. Advanced SQL: SubQueries -- 3.10. Conclusion -- Chapter 4: Example project 1: Querying data with SQL -- 4.1. Introduction and project background -- 4.2. Viewing tables -- 4.3. Querying tables -- 4.3.1. Average number of visits per day per location -- 4.3.2. Average patient age and patient sex per location -- 4.3.3. Average number of patient visits per provider 4.3.4. Counts of diagnosis codes and average age per clinic location -- 4.4. Conclusion -- Chapter 5: Nonrelational databases -- 5.1. Early nonrelational models -- 5.2. The rise of modern nonrelational models -- 5.3. Key-value stores -- 5.4. Document stores -- 5.5. Column stores -- 5.5.1. Traditional column stores -- 5.5.2. Wide column stores -- 5.6. Graph databases -- 5.7. Conclusion -- Reference -- Chapter 6: M/MUMPS -- 6.1. A brief history and context -- 6.2. The M language -- 6.3. General concepts regarding arrays and MUMPS -- 6.4. Arrays and MUMPS 6.5. MUMPS, globals, and data infrastructure -- 6.6. Conclusion -- References -- Section 2: Understanding healthcare data -- Chapter 7: How to approach healthcare data questions -- 7.1. Introduction -- 7.2. Healthcare as a CAS -- 7.3. Drivers of fallacy: Chance and bias -- 7.4. Missingness -- 7.5. Selecting tractable areas for intervention -- 7.6. Data and trust -- 7.7. Conclusion -- Chapter 8: Clinical and administrative workflows: Encounters, laboratory testing, clinical notes, and billing -- 8.1. Introduction -- 8.2. Encounters, patients, and episodes of care Medizinische Informatik (DE-588)4038261-8 gnd |
| subject_GND | (DE-588)4038261-8 (DE-588)4123623-3 |
| title | An introduction to healthcare informatics building data-driven tools |
| title_auth | An introduction to healthcare informatics building data-driven tools |
| title_exact_search | An introduction to healthcare informatics building data-driven tools |
| title_exact_search_txtP | An introduction to healthcare informatics building data-driven tools |
| title_full | An introduction to healthcare informatics building data-driven tools Peter Mccaffrey |
| title_fullStr | An introduction to healthcare informatics building data-driven tools Peter Mccaffrey |
| title_full_unstemmed | An introduction to healthcare informatics building data-driven tools Peter Mccaffrey |
| title_short | An introduction to healthcare informatics |
| title_sort | an introduction to healthcare informatics building data driven tools |
| title_sub | building data-driven tools |
| topic | Medizinische Informatik (DE-588)4038261-8 gnd |
| topic_facet | Medizinische Informatik Lehrbuch |
| url | https://www.sciencedirect.com/science/book/9780128149157 |
| work_keys_str_mv | AT mccaffreypeter anintroductiontohealthcareinformaticsbuildingdatadriventools |