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Добірка наукової літератури з теми "Primary care (Medicine)"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 1 серпня 2024

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Статті в журналах з теми "Primary care (Medicine)"

1

Springer, Michael. "Primary Care Medicine." Archives of Internal Medicine 147, no. 1 (January 1, 1987): 19. http://dx.doi.org/10.1001/archinte.1987.00370010025001.

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2

Sweat, Gregory T. "Primary Care Medicine Recommendations." Mayo Clinic Proceedings 78, no. 6 (June 2003): 793. http://dx.doi.org/10.4065/78.6.789-c.

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3

Bohlmann, Brian J. "Primary Care Internal Medicine." Annals of Internal Medicine 112, no. 11 (June 1, 1990): 884. http://dx.doi.org/10.7326/0003-4819-112-11-884.

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4

Sterling, James C. "Primary Care Sports Medicine." Medicine & Science in Sports & Exercise 26, no. 2 (February 1994): 265. http://dx.doi.org/10.1249/00005768-199402000-00022.

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5

Akau, Cedric K. "Primary Care Sports Medicine." American Journal of Physical Medicine & Rehabilitation 72, no. 6 (December 1993): 409???410. http://dx.doi.org/10.1097/00002060-199312000-00017.

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6

Kersey, Robert D., and Chris Koutures. "Primary-Care Sports Medicine." Athletic Therapy Today 12, no. 1 (January 2007): 7–8. http://dx.doi.org/10.1123/att.12.1.7.

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7

Reingold, J. "Mothers and Primary Care Medicine." PEDIATRICS 117, no. 6 (June 1, 2006): 2323. http://dx.doi.org/10.1542/peds.2006-0389.

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8

Parish, Sharon J., and William H. Salazar. "Sexual Medicine in Primary Care." Annals of Internal Medicine 131, no. 8 (October 19, 1999): 636. http://dx.doi.org/10.7326/0003-4819-131-8-199910190-00036.

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9

Stange, Kurt C., Robert B. Kelly, C. Kent Smith, and Scott Frank. "Preventive medicine in primary care." Postgraduate Medicine 90, no. 3 (September 1991): 125–28. http://dx.doi.org/10.1080/00325481.1991.11701038.

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10

MacAuley, D. "Sports medicine in primary care." British Journal of Sports Medicine 31, no. 3 (September 1, 1997): 208. http://dx.doi.org/10.1136/bjsm.31.3.208.

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Дисертації з теми "Primary care (Medicine)"

1

Bumgarner, D., K. Owens, J. Correll, W. T. Dalton, and Jodi Polaha. "Primary Behavioral Health Care in Pediatric Primary Care." Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/etsu-works/6597.

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2

Polaha, Jodi. "ADHD in Primary Care." Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etsu-works/6690.

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3

Polaha, Jodi. "Primary Care Behavioral Health." Digital Commons @ East Tennessee State University, 2014. https://dc.etsu.edu/etsu-works/6676.

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4

Patel, Vaishal J. "MGH Internal Medicine Associates : primary care redesign." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99015.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2015. In conjunction with the Leaders for Global Operations Program at MIT.
Thesis: S.M., Massachusetts Institute of Technology, Engineering Systems Division, 2015. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 105-107).
Internal Medicine Associates (IMA) is the largest primary care practice at Massachusetts General Hospital (MGH) with over 40 attending physicians, 60 residents, and 80 support staff that deliver care to more than 30,000 patients. The IMA is structured into seven pods that act independently to serve patients. Each pod consists of patient care providers and support staff that work collaboratively in a team structure. In particular, providers and medical assistants work closely together during the clinical session to meet patient needs. A lack of standardization in the practice's operations has contributed to inefficiencies that add to a sense of overload and burnout with the medical assistant staff A detailed study of providers' clinical schedules revealed that individual clinical sessions are highly variable in terms of the number of concurrent clinical sessions per pod, session length, and number of patient appointments booked during this time. Providers in the IMA are part-time and create their clinical schedules based on personal preference and coordination with their other MGH related commitments. Variability in the schedule arises from many systematic, predictable, and unpredictable sources. Additionally, as part of a teaching hospital, IMA supports the educational training of over 60 Internal Medicine residents who hold a varying number of clinical sessions per week, depending on specific requirements of their residency program. Coordinating and supporting provider presence consumes many resources, impacts medical assistant workload, and adds to variability within the practice. The project develops an optimization model to level-load the expected workload on medical assistants and other members of the medical care team by determining the clinic schedules of providers. The expected workload is measured by the number of concurrent sessions and expected number of patient visits per hour. The project has developed an optimization model to suggest changes to the clinic schedule. Specifically in Pod 2/3, by strategically shifting 19.5% of provider sessions, we can achieve an 83% improvement in variability, as measured by the difference between maximum and minimum expected workload. Similar results are modeled for all pods in the IMA. The team has identified a pilot pod to test the model and is
by Vaishal J. Patel.
M.B.A.
S.M.

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5

Dodd, Will. "Primary Care Cases." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etsu-works/8930.

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6

Blackwelder, Reid B. "Primary Care Approach to Cough." Digital Commons @ East Tennessee State University, 2003. https://dc.etsu.edu/etsu-works/6994.

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7

Stratelis, Georgios. "Chronic Obstructive PulmonaryDisease : Early detection and prevention in primary care." Doctoral thesis, Linköpings universitet, Allmänmedicin, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-20736.

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Background and aims. Early detection of Chronic Obstructive Pulmonary Disease (COPD) and secondary prevention by means of smoking cessation are the only available methods of stopping the progression of the disease. The overall aim was to examine the possibilities of early detection and prevention of COPD in General Practice. The specific aims were to evaluate a method of detecting COPD at its early stages, to investigate the rate of emphysema in smokers with normal lung function and smokers defined as preclinical COPD, to investigate the effects of performed spirometries and brief smoking cessation advice on smoking habits and to test if concentrations of certain biomarkers in blood, saliva and exhaled breath condensate (EBC) could identify subjects with COPD or non-COPD subjects supposed to be at risk of developing COPD. Methods. The first study evaluated an invitational method, which offered voluntary screening spirometry to a targeted population of smokers 40-55 years old. In the second follow-up study, all smokers with COPD and half of the smokers with normal lung function (NLF) were annually invited for spirometry and brief smoking cessation advice for a duration of 3 years, with half of the smokers with NLF being tested only last year. In the third study, 54 smokers with NLF were examined with High Resolution Computed Tomography (HRCT), with blood samples also being collected from each subject. In study four, 19 subjects categorised as having COPD, 30 non-COPD subjects and 15 healthy non-smoking volunteers were studied by means of spirometry, DLCO, and analysis of biomarkers in EBC, saliva and serum. Results. A total of 512 smokers responded. The prevalence of COPD was 27.5% and was classified as mild in 85% of the sufferers, moderate in 13% and severe in 2%. At year 1, 10% of the smokers with COPD had been continuously abstinent from smoking, compared to 2% of smokers with NLF. The prolonged abstinence rate increased yearly, and at year 3 the smoking cessation rates in smokers with COPD was 25% compared to 7% in smokers with NLF. By visual analysis, HRCT showed signs of emphysema in 43% of the subjects. Emphysema was also associated with low BMI. Higher serum concentrations of lysozyme and lower DLCO were recorded in those with COPD compared to non-COPD subjects. With the exception of chlorine, none of the remaining biomarkers were detected in EBC. Conclusions. By invitational targeted screening, COPD can be easily detected in its mild stages by using spirometry. By becoming diagnosed with COPD, smokers seem to be more motivated to stop smoking, and COPD patients should repeatedly be offered spirometry and smoking cessation advice which may prevent the progression of the disease to a severe disabling form. HRCT may detect smoke related parenchymal lung damage (i.e. emphysema) in symptom-free smokers with normal spirometry. Serum lysozyme and DLCO appeared to be the strongest discriminator between COPD and non-COPD subjects. The use of EBC as a tool to measure exhaled inflammatory biomarkers involved in COPD is as yet uncertain.

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8

Van, Sickle K., A. Beecham, and Jodi Polaha. "Training a Workforce for Primary Care." Digital Commons @ East Tennessee State University, 2014. https://dc.etsu.edu/etsu-works/6583.

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9

Polaha, Jodi, and Ivy Click. "Conducting Research in Primary Care Settings." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/6752.

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Book Summary: Practice-Based Research shows mental-health practitioners how to establish viable and productive research programs in routine clinical settings. Chapters written by experts in practice-based research use real-world examples to help clinicians work through some of the most common barriers to research output in these settings, including lack of access to institutional review boards, lack of organizational support, and limited access to financial resources. Specialized chapters also provide information on research methods and step-by-step suggestions tailored to a variety of practice settings. This is an essential volume for clinicians interested in establishing successful, long-lasting practice-based research programs.

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10

Polaha, Jodi. "Integrating Behavioral Health Into Primary Care." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etsu-works/6648.

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Книги з теми "Primary care (Medicine)"

1

Goroll, Allan H. Primary care medicine. Philadelphia: Lippincott, 2006.

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2

Goroll, Allan H. Primary care medicine. Philadelphia: Lippincott, 2000.

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3

Brian, Jarman, ed. Primary care. London: Heinemann Medical, 1988.

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4

Pagel, James F., and S. R. Pandi-Perumal, eds. Primary Care Sleep Medicine. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-421-6.

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5

Pagel, James F., and S. R. Pandi-Perumal, eds. Primary Care Sleep Medicine. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1185-1.

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6

1945-, McKeag Douglas, Hough David O, and Zemper Eric D, eds. Primary care sports medicine. Dubuque, IA: Brown & Benchmark, 1993.

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7

1949-, Mladenovic Jeanette, ed. Primary care secrets. 2nd ed. Philadelphia: Hanley & Belfus, 1999.

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8

1949-, Mladenovic Jeanette, ed. Primary care secrets. 3rd ed. Philadelphia, Pa: Hanley & Belfus, 2004.

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9

1949-, Mladenovic Jeanette, ed. Primary care secrets. Philadelphia: Hanley & Belfus, 1995.

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10

Hubbard, John R., and Delmar Short, eds. Primary Care Medicine for Psychiatrists. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5857-6.

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Частини книг з теми "Primary care (Medicine)"

1

Wasserman, Michael R. "Primary Care." In Geriatric Medicine, 1–15. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-01782-8_117-1.

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2

Wasserman, Michael R. "Primary Care." In Geriatric Medicine, 1281–94. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-030-74720-6_117.

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3

Cundiff, David, and Mary Ellen McCarthy. "Primary Care." In The Right Medicine, 101–18. Totowa, NJ: Humana Press, 1994. http://dx.doi.org/10.1007/978-1-4612-0309-4_7.

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4

Idzik, Shannon. "Primary Care." In Encyclopedia of Behavioral Medicine, 1742. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_137.

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5

Idzik, Shannon. "Primary Care." In Encyclopedia of Behavioral Medicine, 1535–36. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_137.

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6

Idzik, Shannon. "Primary Care." In Encyclopedia of Behavioral Medicine, 1–2. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4614-6439-6_137-2.

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7

Idzik, Shannon. "Primary Care." In Encyclopedia of Behavioral Medicine, 1–2. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-6439-6_137-3.

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8

Lunan, Emma Jane, and Andrew Duncan Murray. "Primary Care." In Sport and Exercise Medicine, 223–34. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003179979-18.

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9

White, Kerr L. "Miscellaneous Medicine." In Frontiers of Primary Care, 121–49. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3164-6_5.

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10

Creary, Ludlow, and Ernest Yen. "Primary Care Team." In Urban Family Medicine, 135–38. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4624-4_17.

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Тези доповідей конференцій з теми "Primary care (Medicine)"

1

"Clinical Medicine Undergraduates’ Reluctance to Engage in Primary Care." In 2020 International Conference on Social Sciences and Social Phenomena. Scholar Publishing Group, 2020. http://dx.doi.org/10.38007/proceedings.0001194.

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2

Warren, Warren, and Freedman. "Interviewing expertise in primary care medicine: a knowledge-based support system." In Proceedings of the Twenty-Seventh Annual Hawaii International Conference on System Sciences. IEEE Comput. Soc. Press, 1994. http://dx.doi.org/10.1109/hicss.1994.323354.

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3

Warren, James R., Debra E. Warren, and Richard W. Freedman. "A knowledge-based patient data acquisition system for primary care medicine." In the second international conference. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/170088.170419.

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4

Martin, Matthew, and Randi Sokol. "Evaluation of a National Addiction Medicine Curriculum for Primary Care Physicians." In NAPCRG 50th Annual Meeting — Abstracts of Completed Research 2022. American Academy of Family Physicians, 2023. http://dx.doi.org/10.1370/afm.21.s1.4029.

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5

Nijland, Nicol, Karlijn Cranen, Stef F. F. Verlinden, Saskia M. Kelders, Henk Boer, and Julia E. W. C. van Gemert-Pijnen. "Computer Generated Self-Care Advice via Web-Based Triage of Complaints in Primary Care." In International Conference on eHealth, Telemedicine, and Social Medicine. eTELEMED 2009. IEEE, 2009. http://dx.doi.org/10.1109/etelemed.2009.17.

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6

Borkan, Jeffrey, Elizabeth Smith, Sarita Warrier, Michael Mello, and Meghan Geary. "The Primary Care-Population Medicine (PC-PM) Program at Brown University – Early Outcomes." In NAPCRG 50th Annual Meeting — Abstracts of Completed Research 2022. American Academy of Family Physicians, 2023. http://dx.doi.org/10.1370/afm.21.s1.3605.

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7

Guo, Xu, Han Yu, Chunyan Miao, and Yiqiang Chen. "Agent-based Decision Support for Pain Management in Primary Care Settings." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/943.

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The lack of systematic pain management training and support among primary care physicians (PCPs) limits their ability to provide quality care for patients with pain. Here, we demonstrate an Agent-based Clinical Decision Support System to empower PCPs to leverage knowledge from pain specialists. The system learns a general-purpose representation space on patients, automatically diagnoses pain, recommends therapy and medicine, and suggests a referral program to PCPs in their decision-making tasks.

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8

Mussman, Grant M., Matthew Zackoff, Camille Graham, Paul Bunch, Pierre Manfroy, Paul Korn, Denise Warrick, Jessica Marischen, and Katie Pulda. "Increasing Earlier Verbal Communication Between Hospital Medicine and Primary Care Providers During Hospital Admissions." In Selection of Abstracts From NCE 2016. American Academy of Pediatrics, 2018. http://dx.doi.org/10.1542/peds.141.1_meetingabstract.144.

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9

Помыткина, Татьяна Евгеньевна, Татьяна Александровна Штернис, and Илья Игоревич Селин. "THE PROBLEM OF EMOTIONAL BURNOUT SYNDROME IN PRIMARY CARE PHYSICIANS." In Psychology, Sports science and Medicine (Психология. Спорт. Здравоохранение): сборник статей международной научной конференции (Санкт-Петербург, Октябрь 2022). Crossref, 2022. http://dx.doi.org/10.37539/221030.2022.94.41.007.

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В период пандемии COVID - 19 в 2020 году практическое здравоохранение столкнулось с проблемой синдрома эмоционального выгорания у работников амбулаторно - поликлинических учреждений, что в свою очередь привело к образованию кадрового дефицита. During the COVID -19 pandemic in 2020, practical healthcare faced the problem of emotional burnout among workers in outpatient clinics, which in turn led to the formation of a staffing shortage.

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10

d’Elia, Alexander, Lucy Frith, Mark Gabbay, Sarah Rodgers, Ciara Kierans, and Zoe Colombet. "P02 Artificial Intelligence and Health Inequities in Primary Care: A Scoping Review and Framework." In Society for Social Medicine Annual Scientific Meeting Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jech-2022-ssmabstracts.101.

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Звіти організацій з теми "Primary care (Medicine)"

1

Harris, Daniel M., Stephen D. Tela, and Kris Truong. Provision and Use of Navy Medicine Primary Care: Empirical Background for Optimization. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada402391.

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2

Fadlon, Itzik, and Jessica Van Parys. Primary Care Physician Practice Styles and Patient Care: Evidence from Physician Exits in Medicare. Cambridge, MA: National Bureau of Economic Research, September 2019. http://dx.doi.org/10.3386/w26269.

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3

MacFarlane, Andrew. 2021 medical student essay prize winner - A case of grief. Society for Academic Primary Care, July 2021. http://dx.doi.org/10.37361/medstudessay.2021.1.1.

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As a student undertaking a Longitudinal Integrated Clerkship (LIC)1 based in a GP practice in a rural community in the North of Scotland, I have been lucky to be given responsibility and my own clinic lists. Every day I conduct consultations that change my practice: the challenge of clinically applying the theory I have studied, controlling a consultation and efficiently exploring a patient's problems, empathising with and empowering them to play a part in their own care2 – and most difficult I feel – dealing with the vast amount of uncertainty that medicine, and particularly primary care, presents to both clinician and patient. I initially consulted with a lady in her 60s who attended with her husband, complaining of severe lower back pain who was very difficult to assess due to her pain level. Her husband was understandably concerned about the degree of pain she was in. After assessment and discussion with one of the GPs, we agreed some pain relief and a physio assessment in the next few days would be a practical plan. The patient had one red flag, some leg weakness and numbness, which was her ‘normal’ on account of her multiple sclerosis. At the physio assessment a few days later, the physio felt things were worse and some urgent bloods were ordered, unfortunately finding raised cancer and inflammatory markers. A CT scan of the lung found widespread cancer, a later CT of the head after some developing some acute confusion found brain metastases, and a week and a half after presenting to me, the patient sadly died in hospital. While that was all impactful enough on me, it was the follow-up appointment with the husband who attended on the last triage slot of the evening two weeks later that I found completely altered my understanding of grief and the mourning of a loved one. The husband had asked to speak to a Andrew MacFarlane Year 3 ScotGEM Medical Student 2 doctor just to talk about what had happened to his wife. The GP decided that it would be better if he came into the practice - strictly he probably should have been consulted with over the phone due to coronavirus restrictions - but he was asked what he would prefer and he opted to come in. I sat in on the consultation, I had been helping with any examinations the triage doctor needed and I recognised that this was the husband of the lady I had seen a few weeks earlier. He came in and sat down, head lowered, hands fiddling with the zip on his jacket, trying to find what to say. The GP sat, turned so that they were opposite each other with no desk between them - I was seated off to the side, an onlooker, but acknowledged by the patient with a kind nod when he entered the room. The GP asked gently, “How are you doing?” and roughly 30 seconds passed (a long time in a conversation) before the patient spoke. “I just really miss her…” he whispered with great effort, “I don’t understand how this all happened.” Over the next 45 minutes, he spoke about his wife, how much pain she had been in, the rapid deterioration he witnessed, the cancer being found, and cruelly how she had passed away after he had gone home to get some rest after being by her bedside all day in the hospital. He talked about how they had met, how much he missed her, how empty the house felt without her, and asking himself and us how he was meant to move forward with his life. He had a lot of questions for us, and for himself. Had we missed anything – had he missed anything? The GP really just listened for almost the whole consultation, speaking to him gently, reassuring him that this wasn’t his or anyone’s fault. She stated that this was an awful time for him and that what he was feeling was entirely normal and something we will all universally go through. She emphasised that while it wasn’t helpful at the moment, that things would get better over time.3 He was really glad I was there – having shared a consultation with his wife and I – he thanked me emphatically even though I felt like I hadn’t really helped at all. After some tears, frequent moments of silence and a lot of questions, he left having gotten a lot off his chest. “You just have to listen to people, be there for them as they go through things, and answer their questions as best you can” urged my GP as we discussed the case when the patient left. Almost all family caregivers contact their GP with regards to grief and this consultation really made me realise how important an aspect of my practice it will be in the future.4 It has also made me reflect on the emphasis on undergraduate teaching around ‘breaking bad news’ to patients, but nothing taught about when patients are in the process of grieving further down the line.5 The skill Andrew MacFarlane Year 3 ScotGEM Medical Student 3 required to manage a grieving patient is not one limited to general practice. Patients may grieve the loss of function from acute trauma through to chronic illness in all specialties of medicine - in addition to ‘traditional’ grief from loss of family or friends.6 There wasn’t anything ‘medical’ in the consultation, but I came away from it with a real sense of purpose as to why this career is such a privilege. We look after patients so they can spend as much quality time as they are given with their loved ones, and their loved ones are the ones we care for after they are gone. We as doctors are the constant, and we have to meet patients with compassion at their most difficult times – because it is as much a part of the job as the knowledge and the science – and it is the part of us that patients will remember long after they leave our clinic room. Word Count: 993 words References 1. ScotGEM MBChB - Subjects - University of St Andrews [Internet]. [cited 2021 Mar 27]. Available from: https://www.st-andrews.ac.uk/subjects/medicine/scotgem-mbchb/ 2. Shared decision making in realistic medicine: what works - gov.scot [Internet]. [cited 2021 Mar 27]. Available from: https://www.gov.scot/publications/works-support-promote-shared-decisionmaking-synthesis-recent-evidence/pages/1/ 3. Ghesquiere AR, Patel SR, Kaplan DB, Bruce ML. Primary care providers’ bereavement care practices: Recommendations for research directions. Int J Geriatr Psychiatry. 2014 Dec;29(12):1221–9. 4. Nielsen MK, Christensen K, Neergaard MA, Bidstrup PE, Guldin M-B. Grief symptoms and primary care use: a prospective study of family caregivers. BJGP Open [Internet]. 2020 Aug 1 [cited 2021 Mar 27];4(3). Available from: https://bjgpopen.org/content/4/3/bjgpopen20X101063 5. O’Connor M, Breen LJ. General Practitioners’ experiences of bereavement care and their educational support needs: a qualitative study. BMC Medical Education. 2014 Mar 27;14(1):59. 6. Sikstrom L, Saikaly R, Ferguson G, Mosher PJ, Bonato S, Soklaridis S. Being there: A scoping review of grief support training in medical education. PLOS ONE. 2019 Nov 27;14(11):e0224325.

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Woods, Rachel, Alison Zhong, and Madelyn Vincent. Factors Associated with Influenza & Tdap Vaccine Uptake in Pregnant Patients at the UT Family Medicine Clinic in Memphis. University of Tennessee Health Science Center, 2021. http://dx.doi.org/10.21007/com.lsp.2020.0003.

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INTRODUCTION: Given the increased risk for infections among pregnant patients and newborns, vaccination against influenza (>50,000,000 annual US cases affecting all ages) and pertussis (>15,000 annual US cases disproportionately affecting newborns) are recommended among pregnant patients in order to protect them and their babies via passive immunity to cover a newborn’s window of vaccine ineligibility. Though flu and Tdap vaccination rates among pregnant patients have been trending upwards nationally, there is still room for improvement to achieve optimal rates. OBJECTIVES: The primary objectives were to study factors that affect the vaccination rates at the University of Tennessee Family Medicine Clinic at Memphis (UTFMC-M), compare those rates with national pregnancy flu/Tdap vaccination rates, and to generate recommendations based off observed factors associated with vaccine uptake to improve flu/Tdap vaccination rates in UTFMC-M pregnant patients. METHODS: This was a retrospective chart review of UTFMC-M patients who were pregnant from September 1, 2019-April 24, 2020 (included 2019-2020 flu season) (n=465). Variables studied included demographic data (race, age, insurance), immunization history (vaccine status, history of physician encouragement), and prenatal history (parity, number of prenatal visits, trimester at first visit, high risk clinic (HRC) admittance status). Vaccination status was based on ACIP recommendations (Flu shot eligible = any gestational age; Tdap eligible = ≥27 weeks). Positive HRC admittance was noted for patients with ≥2 visits to the UTFMC-M HRC, a clinic that specializes in high risk pregnant patient care. RESULTS: The patient sample was predominantly black (84.3%) and insured by Medicaid programs (88%). Among eligible UTFMC-M pregnant patients, 50.1% were flu-vaccinated (n=465); 73.8% were Tdap-vaccinated (n=317); and 52.1% were Flu+Tdap-vaccinated (n=317). No significant associations were found between vaccine uptake and HRC status, parity, and age. However, statistically significant relationships were found between vaccine uptake and physician encouragement (positive relationship with flu shot: X2(1, N = 465) =131, p < 0.001, Tdap: X2 (6, N = 465) =476, p < 0.001), number of prenatal visits (flu shot group median 8 visits, Tdap group median 9 visits vs. unvaccinated group median 4 visits; p < 0.001), and early trimester age at first prenatal visit (X2(6, N = 465) =47.635 , p CONCLUSION: 2019-2020 UTFMC-M vaccination rates were on par with 2018-2019 US flu vaccine rates and higher than 2018-2019 US Tdap and Flu+Tdap rates. There were statistically significant relationships between vaccine uptake at UTFMC-M and physician encouragement, number of prenatal visits, and early trimester age at first prenatal visit but no significant relationships with UTFMC-M HRC admittance, parity, or age. Recommendations following from our observations to address further vaccine rate improvement include: continue vaccine encouragement, continue booking multiple visits (8 for flu, 9 for Tdap), prioritize Tdap vaccine higher for late trimester intake patients, and focus on flu vaccine encouragement and education.

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Macinko, James, Inês Dourado, and Frederico C. Guanais. Chronic Diseases, Primary Care and Health Systems Performance: Diagnostics, Tools and Interventions. Inter-American Development Bank, November 2011. http://dx.doi.org/10.18235/0007980.

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Growing exposure to risk factors in combination with low levels of access to preventive care are increasing unmet health needs. LAC has been experiencing a "nutrition transition" towards less healthy diets. Thirty to sixty percent of the region's population does not achieve the minimum recommended levels of physical activity and obesity is rising rapidly. Inadequate access to high quality health services, including clinical prevention and diagnostic services and difficult access to essential medicines are significant contributing factors to the growing burden of chronic disease.

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Cohen, Deborah J., Annette M. Totten, Robert L. Phillips, Jr., Yalda Jabbarpour, Anuradha Jetty, Jennifer DeVoe, Miranda Pappas, Jordan Byers, and Erica Hart. Measuring Primary Healthcare Spending. Agency for Healthcare Research and Quality (AHRQ), May 2024. http://dx.doi.org/10.23970/ahrqepctb44.

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Background. Policy leaders and researchers have identified a range of primary care spending conceptualizations, developed frameworks and methods for measuring primary care spending, and documented the pros and cons of different approaches. However, these efforts have not been comprehensive, particularly as the number of estimates has grown. We continue this work by identifying the definitions, data sources, and approaches used to estimate primary care spending in the United States. Our objective was to identify where there is and is not consensus across methods, and how initial steps toward a standardized approach to estimating primary care spending might be achieved. We approached this comparison from a societal economic perspective. Methods. Searches were conducted in Ovid MEDLINE® and Cochrane CENTRAL databases (inception to May 2, 2023), and were supplemented by manual reviews of reference lists, Scopus searches of key articles, gray literature searches of State and organization websites, and responses to a Federal Register Notice, as well as recommendations from Key Informants. Websites of States and organizations that produced reports were reviewed in November 2023 to identify updates. Publicly available estimates and reports of methods were supplemented by discussions with experts who have supported States’ estimates. Findings. We identified 67 primary care spending estimates for 2010 to 2021: 42 of these were produced by 11 State Governments for their State, 2 were published by the Veterans Health Administration, and 23 were published by researchers or other organizations, which include foundations and policy organizations. Forty-four estimates reported on primary care spending for a single State, one estimate reported spending for the New England States, and 22 reported national spending. To date, 13 State Governments have developed and/or are implementing measurements of primary care spending. When State Governments measure primary care spending, they produce regular, often yearly, estimates. States have produced one to eight estimates, demonstrating some States have more experience with this task than others. Primary care spending estimates in our sample ranged from 3.1 to 10.3 percent. These estimates started with definitions of primary care, which are often labeled narrow or broad. Estimates may use these same labels to mean different things. Narrow definitions of primary care usually include fewer providers, locations, or service types, while broad definitions include more. State, regional, or national estimates are either reported as two estimates, one using a narrow and one using a broad definition of primary care, or as a single estimate labeled neither narrow nor broad. Variations in what providers, services, and locations are included in definitions of primary care are significant and likely contribute to variation in primary care spending estimates. However, it is difficult to distinguish differences in definitions and measurement from differences in actual primary care spending. Conclusions. While there are some core similarities in how primary care spending is measured across State, regional, and national estimates, there are more differences. While there may be rationale behind some of these variations, this variation limits comparisons and what could be understood about the impact of policies. Furthermore, lack of clear, detailed reporting of methods can obscure precisely how and why estimates differ. Research is needed that quantifies the impact different decisions and measurement methods have on spending estimates. To assure the validity and reliability of estimates of primary care spending, and facilitate comparisons and links to health outcomes, Federal, State, and policy leaders need to: (1) collaborate to create a primary care clinician database that can function as a public utility for States to allow for more precise identification of primary care clinics and clinicians, and reduce reliance on Current Procedural Terminology/Healthcare Common Procedure Coding System codes; (2) develop a template for transparent reporting of methods used to estimate primary care spending; (3) foster collaboration among Federal agencies and State leaders to develop a consensus definition of primary care and process for estimating primary care spending, with consideration of methods that are easy to understand and transparent; and (4) support the development and ongoing maintenance of State All-Payer Claims Databases, expand to include nonclaims payments, and supply Medicare and Medicaid estimates for every State.

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Leavy, Michelle B., Costas Boussios, Robert L. Phillips, Jr., Diana Clarke, Barry Sarvet, Aziz Boxwala, and Richard Gliklich. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Final Report. Agency for Healthcare Research and Quality (AHRQ), June 2022. http://dx.doi.org/10.23970/ahrqepcwhitepaperdepressionfinal.

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Objective. The objective of this project was to demonstrate the feasibility and value of collecting harmonized depression outcome measures in the patient registry and health system settings, displaying the outcome measures to clinicians to support individual patient care and population health management, and using the resulting measures data to support patient-centered outcomes research (PCOR). Methods. The harmonized depression outcome measures selected for this project were response, remission, recurrence, suicide ideation and behavior, adverse effects of treatment, and death from suicide. The measures were calculated in the PRIME Registry, sponsored by the American Board of Family Medicine, and PsychPRO, sponsored by the American Psychiatric Association, and displayed on the registry dashboards for the participating pilot sites. At the conclusion of the data collection period (March 2020-March 2021), registry data were analyzed to describe implementation of measurement-based care and outcomes in the primary care and behavioral health care settings. To calculate and display the measures in the health system setting, a Substitutable Medical Apps, Reusable Technology (SMART) on Fast Healthcare Interoperability Resource (FHIR) application was developed and deployed at Baystate Health. Finally a stakeholder panel was convened to develop a prioritized research agenda for PCOR in depression and to provide feedback on the development of a data use and governance toolkit. Results. Calculation of the harmonized outcome measures within the PRIME Registry and PsychPRO was feasible, but technical and operational barriers needed to be overcome to ensure that relevant data were available and that the measures were meaningful to clinicians. Analysis of the registry data demonstrated that the harmonized outcome measures can be used to support PCOR across care settings and data sources. In the health system setting, this project demonstrated that it is technically and operationally feasible to use an open-source app to calculate and display the outcome measures in the clinician’s workflow. Finally, this project produced tools and resources to support future implementations of harmonized measures and use of the resulting data for research, including a prioritized research agenda and data use and governance toolkit. Conclusion. Standardization of outcome measures across patient registries and routine clinical care is an important step toward creating robust, national-level data infrastructure that could serve as the foundation for learning health systems, quality improvement initiatives, and research. This project demonstrated that it is feasible to calculate the harmonized outcome measures for depression in two patient registries and a health system setting, display the results to clinicians to support individual patient management and population health, and use the outcome measures data to support research. This project also assessed the value and burden of capturing the measures in different care settings and created standards-based tools and other resources to support future implementations of harmonized outcome measures in depression and other clinical areas. The findings and lessons learned from this project should serve as a roadmap to guide future implementations of harmonized outcome measures in depression and other clinical areas.

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Leavy, Michelle B., Danielle Cooke, Sarah Hajjar, Erik Bikelman, Bailey Egan, Diana Clarke, Debbie Gibson, Barbara Casanova, and Richard Gliklich. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Report on Registry Configuration. Agency for Healthcare Research and Quality (AHRQ), November 2020. http://dx.doi.org/10.23970/ahrqepcregistryoutcome.

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Background: Major depressive disorder is a common mental disorder. Many pressing questions regarding depression treatment and outcomes exist, and new, efficient research approaches are necessary to address them. The primary objective of this project is to demonstrate the feasibility and value of capturing the harmonized depression outcome measures in the clinical workflow and submitting these data to different registries. Secondary objectives include demonstrating the feasibility of using these data for patient-centered outcomes research and developing a toolkit to support registries interested in sharing data with external researchers. Methods: The harmonized outcome measures for depression were developed through a multi-stakeholder, consensus-based process supported by AHRQ. For this implementation effort, the PRIME Registry, sponsored by the American Board of Family Medicine, and PsychPRO, sponsored by the American Psychiatric Association, each recruited 10 pilot sites from existing registry sites, added the harmonized measures to the registry platform, and submitted the project for institutional review board review Results: The process of preparing each registry to calculate the harmonized measures produced three major findings. First, some clarifications were necessary to make the harmonized definitions operational. Second, some data necessary for the measures are not routinely captured in structured form (e.g., PHQ-9 item 9, adverse events, suicide ideation and behavior, and mortality data). Finally, capture of the PHQ-9 requires operational and technical modifications. The next phase of this project will focus collection of the baseline and follow-up PHQ-9s, as well as other supporting clinical documentation. In parallel to the data collection process, the project team will examine the feasibility of using natural language processing to extract information on PHQ-9 scores, adverse events, and suicidal behaviors from unstructured data. Conclusion: This pilot project represents the first practical implementation of the harmonized outcome measures for depression. Initial results indicate that it is feasible to calculate the measures within the two patient registries, although some challenges were encountered related to the harmonized definition specifications, the availability of the necessary data, and the clinical workflow for collecting the PHQ-9. The ongoing data collection period, combined with an evaluation of the utility of natural language processing for these measures, will produce more information about the practical challenges, value, and burden of using the harmonized measures in the primary care and mental health setting. These findings will be useful to inform future implementations of the harmonized depression outcome measures.

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Skelly, Andrea C., Roger Chou, Joseph R. Dettori, Erika D. Brodt, Andrea Diulio-Nakamura, Kim Mauer, Rongwei Fu, et al. Integrated and Comprehensive Pain Management Programs: Effectiveness and Harms. Agency for Healthcare Research and Quality (AHRQ), October 2021. http://dx.doi.org/10.23970/ahrqepccer251.

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Objectives. To evaluate the effectiveness and harms of pain management programs that are based on the biopsychosocial model of care, particularly in the Medicare population. Data sources. Electronic databases (Ovid® MEDLINE®, PsycINFO®, CINAHL®, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews) from 1989 to May 24, 2021; reference lists; and a Federal Register notice. Review methods. Given lack of consensus on terminology and program definition for pain management, we defined programs as integrated (based in and integrated with primary care) and comprehensive (referral based and separate from primary care) pain management programs (IPMPs and CPMPs). Using predefined criteria and dual review, we selected randomized controlled trials (RCTs) comparing IPMPs and CPMPs with usual care or waitlist, physical activity, pharmacologic therapy, and psychological therapy in patients with complex acute/subacute pain or chronic nonactive cancer pain. Patients needed to have access to medication support/review, psychological support, and physical function support in programs. Meta-analyses were conducted to improve estimate precision. We classified the magnitude of effects as small, moderate, or large based on predefined criteria. Strength of evidence (SOE) was assessed for the primary outcomes of pain, function, and change in opioid use. Results. We included 57 RCTs; 8 evaluated IPMPs and 49 evaluated CPMPs. Compared with usual care or waitlist, IPMPs were associated with small improvements in pain in the short and intermediate term (SOE: low) and in function in the short term (SOE: moderate), but there were no clear differences at other time points. CPMPs were associated with small improvements in pain immediately postintervention (SOE: moderate) but no differences in the short, intermediate, and long term (SOE: low); for function, improvements were moderate immediately postintervention and in the short term; there were no differences in the intermediate or long term (SOE: low at all time points). CPMPs were associated with small to moderate improvements in function and pain versus pharmacologic treatment alone at multiple time frames (SOE: moderate for function intermediate term; low for pain and function at all other times), and with small improvements in function but no improvements in pain in the short term when compared with physical activity alone (SOE: moderate). There were no differences between CPMPs and psychological therapy alone at any time (SOE: low). Serious harms were not reported, although evidence on harms was insufficient. The mean age was 57 years across IPMP RCTs and 45 years across CPMP RCTs. None of the trials specifically enrolled Medicare beneficiaries. Evidence on factors related to program structure, delivery, coordination, and components that may impact outcomes is sparse and there was substantial variability across studies on these factors. Conclusions. IPMPs and CPMPs may provide small to moderate improvements in function and small improvements in pain in patients with chronic pain compared with usual care. Formal pain management programs have not been widely implemented in the United States for general populations or the Medicare population. To the extent that programs are tailored to patients’ needs, our findings are potentially applicable to the Medicare population. Programs that address a range of biopsychosocial aspects of pain, tailor components to patient need, and coordinate care may be of particular importance in this population.

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Peterson, Bradley S., Joey Trampush, Margaret Maglione, Maria Bolshakova, Morah Brown, Mary Rozelle, Aneesa Motala, et al. ADHD Diagnosis and Treatment in Children and Adolescents. Agency for Healthcare Research and Quality (AHRQ), March 2024. http://dx.doi.org/10.23970/ahrqepccer267.

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Objective. The systematic review assessed evidence on the diagnosis, treatment, and monitoring of attention deficit hyperactivity disorder (ADHD) in children and adolescents to inform a planned update of the American Academy of Pediatrics (AAP) guidelines. Data sources. We searched PubMed®, Embase®, PsycINFO®, ERIC, clinicaltrials.gov, and prior reviews for primary studies published since 1980. The report includes studies published to June 15, 2023. Review methods. The review followed a detailed protocol and was supported by a Technical Expert Panel. Citation screening was facilitated by machine learning; two independent reviewers screened full text citations for eligibility. We abstracted data using software designed for systematic reviews. Risk of bias assessments focused on key sources of bias for diagnostic and intervention studies. We conducted strength of evidence (SoE) and applicability assessments for key outcomes. The protocol for the review has been registered in PROSPERO (CRD42022312656). Results. Searches identified 23,139 citations, and 7,534 were obtained as full text. We included 550 studies reported in 1,097 publications (231 studies addressed diagnosis, 312 studies addressed treatment, and 10 studies addressed monitoring). Diagnostic studies reported on the diagnostic performance of numerous parental ratings, teacher rating scales, teen/child self-reports, clinician tools, neuropsychological tests, EEG approaches, imaging, and biomarkers. Multiple approaches showed promising diagnostic performance (e.g., using parental rating scales), although estimates of performance varied considerably across studies and the SoE was generally low. Few studies reported estimates for children under the age of 7. Treatment studies evaluated combined pharmacological and behavior approaches, medication approved by the Food and Drug Administration, other pharmacologic treatment, psychological/behavioral approaches, cognitive training, neurofeedback, neurostimulation, physical exercise, nutrition and supplements, integrative medicine, parent support, school interventions, and provider or model-of-care interventions. Medication treatment was associated with improved broadband scale scores and ADHD symptoms (high SoE) as well as function (moderate SoE), but also appetite suppression and adverse events (high SoE). Psychosocial interventions also showed improvement in ADHD symptoms based on moderate SoE. Few studies have evaluated combinations of pharmacological and youth-directed psychosocial interventions, and we did not find combinations that were systematically superior to monotherapy (low SoE). Published monitoring approaches for ADHD were limited and the SoE is insufficient. Conclusion. Many diagnostic tools are available to aid the diagnosis of ADHD, but few monitoring strategies have been studied. Medication therapies remain important treatment options, although with a risk of side effects, as the evidence base for psychosocial therapies strengthens and other nondrug treatment approaches emerge.

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