The Impact of Telehealth on Primary Care Physician Panel Sizes: A Modeling Study.

INTRODUCTION: Most research on the use of telehealth in lieu of in-office visits has focused on its growth, its impact on access, and the experience of physicians and patients. One important issue that has not gotten much attention is the potential for telehealth to significantly increase physician capacity by reducing nonvalue adding activities and patient no-shows. We explore this in this article. METHODS: We use data from the electronic health records of 2 health care systems and information gathered from family medicine physician focus groups to develop estimates of visit durations and no-show rates for tele-visits. We use these in a simulation model to determine how patient panel sizes could be increased while maintaining high levels of access by substituting tele-visits for in-person visits. RESULTS: We found that tele-visits reduce the nonvalue-added time physicians spend with patients as well as patient no-shows. At current levels of tele-visit utilization, the use of tele-visits may translate into more than a 10% increase in patient panel sizes assuming a modest reduction in visit durations and no-shows, and as much as a 30% increase assuming that half of all visits could be effectively conducted virtually and result in a greater reduction in visit durations and no-shows. DISCUSSION: Our study provides evidence that a major benefit of using telehealth for many routine encounters is a reduction in wasted physician time and a substantial increase in the number of patients that a primary care physician can care for without jeopardizing access to care.

The Impact of Step-Down Unit Care on Patient Outcomes After ICU Discharge.

OBJECTIVES: To examine whether and how step-down unit admission after ICU discharge affects patient outcomes. DESIGN: Retrospective study using an instrumental variable approach to remove potential biases from unobserved differences in illness severity for patients admitted to the step-down unit after ICU discharge. SETTING: Ten hospitals in an integrated healthcare delivery system in Northern California. PATIENTS: Eleven-thousand fifty-eight episodes involving patients who were admitted via emergency departments to a medical service from July 2010 to June 2011, were admitted to the ICU at least once during their hospitalization, and were discharged from the ICU to the step-down unit or the ward. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Using congestion in the step-down unit as an instrumental variable, we quantified the impact of step-down unit care in terms of clinical and operational outcomes. On average, for ICU patients with lower illness severity, we found that availability of step-down unit care was associated with an absolute decrease in the likelihood of hospital readmission within 30 days of 3.9% (95% CI, 3.6-4.1%). We did not find statistically significant effects on other outcomes. For ICU patients with higher illness severity, we found that availability of step-down unit care was associated with an absolute decrease in in-hospital mortality of 2.5% (95% CI, 2.3-2.6%), a decrease in remaining hospital length-of-stay of 1.1 days (95% CI, 1.0-1.2 d), and a decrease in the likelihood of ICU readmission within 5 days of 3.6% (95% CI, 3.3-3.8%). CONCLUSIONS: This study shows that there exists a subset of patients discharged from the ICU who may benefit from care in an step-down unit relative to that in the ward. We found that step-down unit care was associated with statistically significant improvements in patient outcomes especially for high-risk patients. Our results suggest that step-down units can provide effective transitional care for ICU patients.

A study of New York City obstetrics units demonstrates the potential for reducing hospital inpatient capacity.

Hospitals are under significant pressure from payers to reduce costs. The single largest fixed cost for a hospital is inpatient beds, yet there is significant variation in hospital capacity utilization. We study bed capacity in New York City hospital obstetrics units and find that while many hospitals have an insufficient number of beds to provide timely access to care, overall there is significant excess capacity. Our findings, coupled with current demographic and clinical practice trends, indicate that a large fraction of obstetrics units nationwide could likely reduce their bed capacity while assuring timely access to care, resulting in large savings in capital and staffing costs. Given emerging health care delivery and payment models that will likely decrease demand for other types of hospital beds, our study suggests that data-based methodologies should be used by hospitals and policy makers to identify opportunities for reducing excess bed capacity in other inpatient units as well.

Primary care physician shortages could be eliminated through use of teams, nonphysicians, and electronic communication.

Most existing estimates of the shortage of primary care physicians are based on simple ratios, such as one physician for every 2,500 patients. These estimates do not consider the impact of such ratios on patients' ability to get timely access to care. They also do not quantify the impact of changing patient demographics on the demand side and alternative methods of delivering care on the supply side. We used simulation methods to provide estimates of the number of primary care physicians needed, based on a comprehensive analysis considering access, demographics, and changing practice patterns. We show that the implementation of some increasingly popular operational changes in the ways clinicians deliver care-including the use of teams or "pods," better information technology and sharing of data, and the use of nonphysicians-have the potential to offset completely the increase in demand for physician services while improving access to care, thereby averting a primary care physician shortage.

Burn disaster response planning in New York City: updated recommendations for best practices.

Since its inception in 2006, the New York City (NYC) Task Force for Patients with Burns has continued to develop a city-wide and regional response plan that addressed the triage, treatment, transportation of 50/million (400) adult and pediatric victims for 3 to 5 days after a large-scale burn disaster within NYC until such time that a burn center bed and transportation could be secured. The following presents updated recommendations on these planning efforts. Previously published literature, project deliverables, and meeting documents for the period of 2009-2010 were reviewed. A numerical simulation was designed to evaluate the triage algorithm developed for this plan. A new, secondary triage scoring algorithm, based on co-morbidities and predicted outcomes, was created to prioritize multiple patients within a given acuity and predicted survivability cohort. Recommendations for a centralized patient and resource tracking database, plan operations, activation thresholds, mass triage, communications, data flow, staffing, resource utilization, provider indemnification, and stakeholder roles and responsibilities were specified. Educational modules for prehospital providers and nonburn center nurses and physicians who would provide interim care to burn injured disaster victims were created and pilot tested. These updated best practice recommendations provide a strong foundation for further planning efforts, and as of February 2011, serve as the frame work for the NYC Burn Surge Response Plan that has been incorporated into the New York State Burn Plan.

The impact of ambulance diversion on heart attack deaths.

Hospital ambulance diversions are prevalent and increasing nationwide as emergency departments experience growing congestion. Using negative binomial regressions, this paper links the number of acute myocardial infarction (AMI) deaths to the level and extent of diversion in the five boroughs of New York City. The results indicate that both high levels of ambulance diversion and simultaneous diversion across hospitals are associated with increasing numbers of deaths from AMI.

Providing timely access to care: what is the right patient panel size?

BACKGROUND: Delays for appointments are prevalent, resulting in patient dissatisfaction, higher costs, and possible adverse clinical consequences. A "just-in-time" approach to patient scheduling, called advanced access, has been effective in reducing delays in multiple clinical settings. Offering most patients appointments on the same day requires achieving an appropriate balance between supply of and demand for appointments, but no methods have been previously proposed to determine what this balance should be. METHODS: A measure of balance is termed the overflow frequency level--the fraction of days when demand exceeds the average number of appointment slots available. A probability model was developed to estimate this measure for any practice. The model can be used in identifying an appropriate panel size or, conversely, the physician capacity needed to provide timely access. RESULTS: Delays for appointments will be excessive unless the ratio of the average daily demand for appointments to the average daily capacity is less than one. This ratio's appropriate value is dependent on the desired overflow frequency level, which indicates the fraction of days for which physician overtime would be necessary to offer most patients same-day appointments. A table provides suggested panel sizes for a range of practice types, and a spreadsheet file is available on request to help determine panel size or physician capacity in any specific situation. CONCLUSION: The simple probability model can be used to improve the timeliness of care while considering the constraints on physicians' working hours.

Using queueing theory to increase the effectiveness of emergency department provider staffing.

OBJECTIVES: Significant variation in emergency department (ED) patient arrival rates necessitates the adjustment of staffing patterns to optimize the timely care of patients. This study evaluated the effectiveness of a queueing model in identifying provider staffing patterns to reduce the fraction of patients who leave without being seen. METHODS: The authors collected detailed ED arrival data from an urban hospital and used a Lag SIPP queueing analysis to gain insights on how to change provider staffing to decrease the proportion of patients who leave without being seen. The authors then compared this proportion for the same 39-week period before and after the resulting changes. RESULTS: Despite an increase in arrival volume of 1,078 patients (6.3%), an average increase in provider hours of 12 hours per week (3.1%) resulted in 258 fewer patients who left without being seen. This represents a decrease in the proportion of patients who left without being seen by 22.9%. Restricting attention to a four-day subset of the week during which there was no increase in total provider hours, a reallocation of providers based on the queueing model resulted in 161 fewer patients who left without being seen (21.7%), despite an additional 548 patients (5.5%) arriving in the second half of the study. CONCLUSIONS: Timely access to a provider is a critical dimension of ED quality performance. In an environment in which EDs are often understaffed, analyses of arrival patterns and the use of queueing models can be extremely useful in identifying the most effective allocation of staff.

How many hospital beds?

For many years, average bed occupancy level has been the primary measure that has guided hospital bed capacity decisions at both policy and managerial levels. Even now, the common wisdom that there is an excess of beds nationally has been based on a federal target of 85% occupancy that was developed about 25 years ago. This paper examines data from New York state and uses queueing analysis to estimate bed unavailability in intensive care units (ICUs) and obstetrics units. Using various patient delay standards, units that appear to have insufficient capacity are identified. The results indicate that as many as 40% of all obstetrics units and 90% of ICUs have insufficient capacity to provide an appropriate bed when needed. This contrasts sharply with what would be deduced using standard average occupancy targets. Furthermore, given the model's assumptions, these estimates are likely to be conservative. These findings illustrate that if service quality is deemed important, hospitals need to plan capacity based on standards that reflect the ability to place patients in appropriate beds in a timely fashion rather than on target occupancy levels. Doing so will require the collection and analysis of operational data-such as demands for and use of beds, and patient delays--which generally are not available.