Analytical models to determine room requirements in outpatient clinics

Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room while patients visit for consultation, we call this the Patient-to-Doctor policy (PtD-policy). A different approach is the Doctor-to-Patient policy (DtP-policy), whereby the doctor travels between multiple consultation rooms, in which patients prepare for their consultation. In the latter approach, the doctor saves time by consulting fully prepared patients. We use a queueing theoretic and a discrete-event simulation approach to provide generic models that enable performance evaluations of the two policies for different parameter settings. These models can be used by managers of outpatient clinics to compare the two policies and choose a particular policy when redesigning the patient process.We use the models to analytically show that the DtP-policy is superior to the PtD-policy under the condition that the doctor’s travel time between rooms is lower than the patient’s preparation time. In addition, to calculate the required number of consultation rooms in the DtP-policy, we provide an expression for the fraction of consultations that are in immediate succession; or, in other words, the fraction of time the next patient is prepared and ready, immediately after a doctor finishes a consultation. We apply our methods for a range of distributions and parameters and to a case study in a medium-sized general hospital that inspired this research

Cleveland Hospital Systems Expand Despite Weak Economy

Tracks developments in metropolitan Cleveland's healthcare market during the recession, including capacity expansions at Cleveland Clinic and University Hospitals, shifting of costs from employers to employees, pressure on the safety net, and reform

An analytical comparison of the patient-to-doctor policy and the doctor-to-patient policy in the outpatient clinic

Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room, while patients visit for consultation, we call this the Patient-to-Doctor policy. A different approach is the Doctor-to-Patient policy, whereby the doctor travels between multiple consultation rooms, in which patients prepare for their consultation. In the latter approach, the doctor saves time by consulting fully prepared patients. We compare the two policies via a queueing theoretic and a discrete-event simulation approach. We analytically show that the Doctor-to-Patient policy is superior to the Patient-to-Doctor policy under the condition that the doctor’s travel time between rooms is lower than the patient’s preparation time. Simulation results indicate that the same applies when the average travel time is lower than the average preparation time. In addition, to calculate the required number of consultation rooms in the Doctor-to-Patient policy, we provide an expression for the fraction of consultations that are in immediate succession; or, in other words, the fraction of time the next patient is prepared and ready, immediately after a doctor finishes a consultation.We apply our methods for a range of distributions and parameters and to a case study in a medium-sized general hospital that inspired this research

Gundersen Lutheran Health System: Performance Improvement Through Partnership

Highlights Fund-defined attributes of an ideal system and best practices such as using data for benchmarking, increasing transparency, and driving improvement; investing in primary care and disease management; and hiring engineers to improve operations

Mayo Clinic: Multidisciplinary Teamwork, Physician-Led Governance, and Patient-Centered Culture Drive World-Class Health Care

Describes Fund-defined attributes of an ideal care delivery system, Mayo's model of multidisciplinary practice with salary-based compensation, and best practices, including a shared electronic health record and innovations to implement research quickly

An integrated care pathway for menorrhagia across the primary–secondary interface : patients' experience, clinical outcomes, and service utilisation

Background: ‘‘Referral’’ characterises a significant area of interaction between primary and secondary care. Despite advantages, it can be inflexible, and may lead to duplication. Objective: To examine the outcomes of an integrated model that lends weight to general practitioner (GP)-led evidence based care. Design: A prospective, non-random comparison of two services: women attending the new (Bridges) pathway compared with those attending a consultant-led one-stop menstrual clinic (OSMC). Patients’ views were examined using patient career diaries, health and clinical outcomes, and resource utilisation. Follow-up was for 8 months. Setting: A large teaching hospital and general practices within one primary care trust (PCT). Results: Between March 2002 and June 2004, 99 women in the Bridges pathway were compared with 94 women referred to the OSMC by GPs from non-participating PCTs. The patient career diary demonstrated a significant improvement in the Bridges group for patient information, fitting in at the point of arrangements made for the patient to attend hospital (ease of access) (p,0.001), choice of doctor (p = 0.020), waiting time for an appointment (p,0.001), and less ‘‘limbo’’ (patient experience of non-coordination between primary and secondary care) (p,0.001). At 8 months there were no significant differences between the two groups in surgical and medical treatment rates or in the use of GP clinic appointments. Significantly fewer (traditional) hospital outpatient appointments were made in the Bridges group than in the OSMC group (p,0.001). Conclusion: A general practice-led model of integrated care can significantly reduce outpatient attendance while improving patient experience, and maintaining the quality of care

Predictive modeling of housing instability and homelessness in the Veterans Health Administration

OBJECTIVE: To develop and test predictive models of housing instability and homelessness based on responses to a brief screening instrument administered throughout the Veterans Health Administration (VHA). DATA SOURCES/STUDY SETTING: Electronic medical record data from 5.8 million Veterans who responded to the VHA's Homelessness Screening Clinical Reminder (HSCR) between October 2012 and September 2015. STUDY DESIGN: We randomly selected 80% of Veterans in our sample to develop predictive models. We evaluated the performance of both logistic regression and random forests—a machine learning algorithm—using the remaining 20% of cases. DATA COLLECTION/EXTRACTION METHODS: Data were extracted from two sources: VHA's Corporate Data Warehouse and National Homeless Registry. PRINCIPAL FINDINGS: Performance for all models was acceptable or better. Random forests models were more sensitive in predicting housing instability and homelessness than logistic regression, but less specific in predicting housing instability. Rates of positive screens for both outcomes were highest among Veterans in the top strata of model‐predicted risk. CONCLUSIONS: Predictive models based on medical record data can identify Veterans likely to report housing instability and homelessness, making the HSCR screening process more efficient and informing new engagement strategies. Our findings have implications for similar instruments in other health care systems.U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Grant/Award Number: IIR 13-334 (IIR 13-334 - U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSRD))Accepted manuscrip

Factors determining patients’ intentions to use point-of-care testing medical devices for self-monitoring: The case of international normalised ratio self-testing

This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited. - Copyright @ 2012 Dove Medical Press LtdThis article has been made available through the Brunel Open Access Publishing Fund.Purpose: To identify factors that determine patients' intentions to use point-of-care medical devices, ie, portable coagulometer devices for self-testing of the international normalized ratio (INR) required for ongoing monitoring of blood-coagulation intensity among patients on long-term oral anticoagulation therapy with vitamin K antagonists, eg, warfarin. Methods: A cross-sectional study that applied the technology-acceptance model through a self-completed questionnaire, which was administered to a convenience sample of 125 outpatients attending outpatient anticoagulation services at a district general hospital in London, UK. Data were analyzed using descriptive statistics, factor analyses, and structural equation modeling. Results: The participants were mainly male (64%) and aged ≥ 71 years (60%). All these patients were attending the hospital outpatient anticoagulation clinic for INR testing; only two patients were currently using INR self-testing, 84% of patients had no knowledge about INR self-testing using a portable coagulometer device, and 96% of patients were never offered the option of the INR self-testing. A significant structural equation model explaining 79% of the variance in patients’ intentions to use INR self-testing was observed. The significant predictors that directly affected patients' intention to use INR self-testing were the perception of technology (β = 0.92, P < 0.001), trust in doctor (β = −0.24, P = 0.028), and affordability (β = 0.15, P = 0.016). In addition, the perception of technology was significantly affected by trust in doctor (β = 0.43, P = 0.002), age (β = −0.32, P < 0.001), and affordability (β = 0.23, P = 0.013); thereby, the intention to use INR self-testing was indirectly affected by trust in doctor (β = 0.40), age (β = −0.29), and affordability (β = 0.21) via the perception of technology. Conclusion: Patients’ intentions to use portable coagulometers for INR self-testing are affected by patients' perceptions about the INR testing device, the cost of device, trust in doctors/clinicians, and the age of the patient, which need to be considered prior to any intervention involving INR self-testing by patients. Manufacturers should focus on increasing the affordability of INR testing devices for patients’ self-testing and on the potential role of medical practitioners in supporting use of these medical devices as patients move from hospital to home testing.This study is funded by the Multidisciplinary Assessment of Technology Centre for Healthcare (MATCH) program (EPSRC grant EP/GO12393/1)