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The Impact of an Insurance Administration-Free Primary Care Office on Hospital Admissions: A Community-Level Comparison With Traditional Fee-for-Service Family Practice Groups

Authors:

Abstract

This study compares hospital admissions over a 3-year period (2009-2011) between a community's 2 major private, fee-for-service physician groups and an insurance administration-free, hospital-affiliated clinic designed to provide a full array of primary care services to low-income individuals at little or no cost. We use data on patients' chronic conditions and inpatient hospital admissions to compare patients' average number of physician office visits and overall hospital admission rates per 1000 patients. The data indicate that while clinic patients have a higher (or equal) average number of chronic conditions compared with patients in the private physician groups, they exhibit lower hospital admission rates. Clinic patients also exhibit a higher average annual frequency of physician visits. Results of this study suggest that enhanced access to primary care could help mitigate inefficient use of non-urgent care hospital resources for the uninsured and reduce costly hospitalizations even in the short run.
The Impact of an Insurance Administration-Free Primary Care Office on
Hospital Admissions: A Community-Level Comparison to
Traditional Fee-for-Service Family Practice Groups
by
Mark D. Agee, Ph.D. Zane Gates, M.D.
Department of Economics UPMC Altoona
Pennsylvania State University 620 Howard Avenue
Altoona, PA 16601 Altoona, PA 16601
mda4@psu.edu zanegates3@gmail.com
Keywords: Healthcare for the Uninsured, Hospital Admissions, Frequency of Office Visits, Case
Study, Insurance-Free Office, Healthcare Finance
Citation:
Agee, M. D., & Gates, Z. (2014). The Impact of an Insurance Administration–Free Primary Care
Office on Hospital Admissions: A Community-Level Comparison to Traditional Fee-for-Service
Family Practice Groups. Journal of Primary Care & Community Health, 5(3), 202-207.
2
Abstract
This study compares hospital admissions over a three year period (2009-2011) between a
community’s two major private, fee-for-service physician groups and an insurance
administration-free, hospital-affiliated clinic designed to provide a full array of primary care
services to low-income individuals at little or no cost. We use data on patients’ chronic
conditions and inpatient hospital admissions to compare patients’ average number of physician
office visits and overall hospital admission rates per 1000 patients. The data indicate that while
clinic patients have a higher (or equal) average number of chronic conditions compared with
patients in the private physician groups, they exhibit lower hospital admission rates. Clinic
patients also exhibit a higher average annual frequency of physician visits. Results of this study
suggest that enhanced access to primary care could help mitigate inefficient use of non-urgent
care hospital resources for the uninsured and reduce costly hospitalizations even in the short-run.
3
1. Introduction
Administrative costs in U.S. primary care offices have been growing steadily over the last
forty years without improvements in overall patient health. Obesity and DM rates continue to
rise and according to The World Health Report (2000), the U.S. health system ranked 37th out of
its 191 members in health outcomes [1]. The percent spent on billing and insurance in a single
specialty primary care office has risen to 14.5% of total revenue according to Kahn et al. [2].
While health insurance plans have taken steps to reduce the administrative time burdens they
place on physicians and clinical staff, physicians spend nearly three weeks per year, and nursing
staff nearly twenty three weeks per year, interacting with health plans. This time cost is
especially large for primary care offices, particularly small offices [3].
This paper examines the potential health outcomes benefits of a new approach to
ambulatory medicine, an insurance administration-free primary care office. The current patient-
centered medical home (PCMH) model initiatives around the country have shown promise [3,4],
although results vary [5,6]. Geisinger’s PCMH has shown a decrease of 56 admissions per 1000
patients in hospital admissions using the coordinated method [7,8]. The home model utilizes
nurse managers who coordinate the care between the patients, primary care physicians, and
subspecialists. However, managers are also responsible for maneuvering patients through their
individual insurance plans. The more variable the covered benefits and associated time costs
needed to administer patients’ insurance plans, the more difficult it is for managers to execute a
truly coordinated treatment plan. Also, cost is a key factor in patients’ choice of treatment plans.
With the advent of high deductible plans in the health exchanges, the RAND study showed that
with a $1000 dollar deductible policy, both low and high income patients chose to forgo certain
tests and procedures important to their preventive care [9]. This contrasts with mounting
4
evidence that increased interaction with healthcare professionals leads to improvements in
patient well behaviors and health outcomes [10-13].
2. Study, Data, and Methods
This study compares Partnering for Health Services (PHS), a completely insurance
administration-free office located in Altoona, Pennsylvania, to the two largest primary care
groups in the Altoona, Blair County, Pennsylvania region: Blair Medical Associates (BMA), and
Mainline Medical Associates (MMA). BMA and MMA provide primary care under the
traditional insurance fee-for-service payment method to almost 60% of Blair County’s 127, 121
residents [14]. BMA is a large multi-specialty group with a total of 40 physicians. We limit our
focus to the family practice portion of this group which consists of 16.5 fulltime equivalents
(FTE’S). MMA is Altoona’s second largest family practice group with 12.84 FTE’s. BMA and
MMA follow a patient procedure common to most insurance fee-for-service primary care offices
in the U.S.: The patient enters the office and is met by a receptionist who must process their
insurance information. The patient is then escorted by a nurse to an examination room. In the
examination room, the nurse assesses the patient’s perceived clinical symptoms and insurance
coverage. The physician then examines the patient and designs a treatment plan around the
patient’s insurance coverage. Finally, the patient stops at the receptionist a second time to pay
co-pays required by insurance. After the patient leaves the office a nurse must contact the
insurance company via telephone or electronically for approval of the treatment plan and any
tests ordered by the physician. The nurse must also inform the patient of the treatment plan and
discuss any barriers to compliance of the treatment plan due to cost.
5
PHS is a hospital-based family practice clinic started by Altoona Regional Health System
(ARHS) in 1999 as a way to divert uninsured patients away from the emergency department for
non-urgent services yet still provide the care they need. The clinic does not accept health
insurance for its primary care services, even though approximately 30% of PHS patients carry
hospitalization-only coverage. PHS is not a free clinic; rather it functions as a traditional full-
service doctor’s office, open 4.5 days per week, providing all types of primary care services,
diagnostic services, medications, and referrals to specialists within its network. PHS is an
affiliate of ARHS as a cooperative effort among ARHS, volunteer physicians, full-time paid
physician assistants, and patients. Patients are accepted into the practice by proof of no primary
care insurance, have household income up to 300% of the federal poverty level, and do not
qualify for Medicaid. For unlimited visits to the clinic with no co-pays or deductibles, patients
pay a monthly capitation fee based on income. Patients with household income up to 150% of
poverty level pay no fee; patients with income up to 300% of poverty level pay $99 per month.
Small business owners can also purchase an employee-based plan for $169 per month per
employee.
PHS’s patient procedure differs from that of a traditional insurance fee-for-service
primary care office. Initially, each patient is assessed clinically by a nurse. The physician then
examines the patient without insurance influence and designs a treatment plan based solely on
the clinical criteria set forth by the PHS providers using ACP guidelines. After the examination,
the patient meets one-on-one with a nurse care coordinator (or “nurse closer”) who counsels the
patient about the treatment plan and the patient’s role in the plan, reviews medications, sets up
referral appointments with any specialists, and orders all tests, prescriptions, and refills. The
patient then leaves the office without co-pays or deductibles. Clinic patients who are diagnosed
6
with chronic illnesses are encouraged to have frequent visits to the clinic. Dieticians and diabetic
educators are also embedded into the clinic’s model as part of a comprehensive treatment plan.
The forgoing analysis uses three years of data on unique (currently active) patients’
chronic health conditions, practice FTE’s, number of patient visits, and number of inpatient
hospital admissions to compare the PHS clinic to BMA and MMA in terms of overall patient
health and hospital admission rates. Data consist of all patients in the 18-64 age range currently
active at PHS, BMA, or MMA from 2009-2011. Hospital admission rates are compared among
the practices using average admissions per provider per 1000 patients.
Prevalence of Chronic Disease. Table 1 provides some basic summary health statistics
on the patient populations based on data obtained from the CFO’s of MMA and BMA [personal
communications, Val Mignogia, CEO, MMA; Charles Zorger, CFO, ARHS], and from the PHS
clinic nurse manager and hospital billing department [personal communication, Cloyd Beers,
Director]. We calculate group percentages of chronic diseases from the top five diagnoses
outlined by Vital and Health Statistics from NCHS [15]. The five diagnoses determined by ICD-
9 codes of each patient visit are Hypertension (HTN), Cerebral Vascular Accident (CVA),
Coronary Artery disease (CAD), Diabetes (DM), and Chronic Obstructive Pulmonary Disease
(COPD). Percentages are calculated as the annual number of unique patients with each disease
divided by total annual unique patients. Since the annual percentages exhibit very little variation
over the 2009-2011 time range, Table 1 figures represent three-year averages.
7
Table 1. Provider Population Percentages by Chronic Disease Diagnosisa
PHS
MMA
Difference
from PHS
P
BMA
Difference
P
HTN
36.8
43.0
-7.0
<.001
35.6
0.438
CVA
2.5
2.4
0.1
0.795
2.0
0.243
CAD
7.6
9.7
-2.1
0.031
8.3
0.442
DM
12.7
18.9
-6.2
<.001
12.2
0.656
COPD
16.6
4.8
11.8
<.001
3.4
<.001
Average
Household
Income
$28,848
$41,980
-$13,132
<.001
$43,243
<.001
aGroup percentages were compared using the
2
χ
test; household income was compared using the
t test.
In Table 1, group percentages of HTN, DM, and CAD were significantly higher among
MMA patients. According to HCUP [16], CAD is the second leading cause of a hospital
admission. Among PHS patients, the prevalence of COPD (at 16.6%), the sixth leading cause of
a hospital admission, is significantly higher than BMA and MMA patients. BMA patients
exhibit the lowest prevalence of 4 of the 5 diseases, but do not differ significantly from PHS
patients with the exception of COPD prevalence. While MMA patients claim the highest
prevalence in 3 of the 5 categories, PHS patients exhibit the widest between-practice disparity in
COPD cases. One plausible explanation for this disparity is household income, as higher COPD
rates have been found to occur among unemployed or low income workers who either smoke or
work in jobs that expose them to dust or other respiratory hazards [17]. However, with regard to
the other four diagnoses, no such income gradient is apparent.
Frequency of Provider Visits. Frequency of provider visits per patient was calculated
using data on actual appointments confirmed within the specific calendar year for all unique
patients ages 18-64. Since there were only slight annual differences in visit numbers across
8
PHS, BMA, and MMA over the 2009-2011 period, numbers are calculated as three-year
averages. In addition, due to slight differences in the number of unique patients per provider,
patient numbers were rounded to the nearest thousand to maintain consistency. This rounding
actually led to slightly lower average annual visits for PHS and higher average annual visits for
BMA and MMA patients.
Table 2. Average Annual Provider Visits for Unique Patients Age 18-64, 2009-2011
Provider
Total Unique
Patients Total Visits Average Annual Provider Visits
BMA
17,074
46,100
2.70
MMA
12,938
32,394
2.50
PHS
986
4,860
4.93
Table 2 shows BMA with a total of 46,100 visits from 17,074 unique patients averaging
2.7 visits per patient per year from 2009 to 2011. MMA recorded slightly lower average visits at
2.5 for its 12,938 unique patients. PHS recorded the highest number of average visits, totaling
4860 visits from its 986 unique patients, nearly twice that of MMA and BMA. One explanation
for this difference could be the PHS clinic’s design, which encourages patients to visit the clinic
often until control over their chronic illness is established. Another explanation might be that
PHS patients, once accepted, encounter no insurance-related access problems; also, clinic
physicians and staff engage in no insurance administration activities thus enabling them to see
more patients.
Hospital Admissions. PHS, BMA, and MMA use the same hospitalist service,
Lexington Hospitalist, for their inpatient admissions. The eleven-group hospitalist rotates shifts
and has standing orders for most responsibilities including determination of the appropriateness
9
and the coordination of patient admissions and follow-up visits, providing bedside care,
managing consultations and communications with specialists, ordering labs and procedures, and
managing the discharge of patients [6]. Lexington Hospitalist has no access to insurance
information of the patient, unless they request it, and treat each patient with predesigned
treatment protocols to assure no variability in management of patients amongst the primary care
physician, regardless of practice. Lexington Hospitalist provided data on admissions per each of
the three providers for the years 2009-2011. Data provided by the Altoona Regional Hospital’s
billing services was used to cross-check Lexington Hospitalist data for accuracy. Table 3
tabulates for each practice the average annual admissions per provider per 1000 patients.
Table 3. Average Annual Hospital Admissions per Provider per 1000 Patients
Year
PHS
MMA
Difference
from PHS
P
95%
Confidence
Interval
BMA
Difference
from PHS
P
95%
Confidence
Interval
2009
24
43.6
-19.6
0.004
-8.5, -30.0
50.9
-26.9
<.001
-15.9, -37.3
2010
34
50.8
-16.8
0.023
-3.8, -28.8
52
-18.0
0.015
-5.1, -29.9
2011
22
49.2
-27.2
<.001
-16.4, -37.4
51.5
-29.5
<.001
-18.8, -39.5
2009-
2011
Average
26.67
47.87
-21.2
0.002
-10.3, -32.7
51.47
-24.8
<.001
-14.0, -36.2
The average annual admission rate for BMA patients varied only slightly from 2009 to
2011 with a three-year average of 51.47 admissions per provider per 1000 patients. MMA’s rate
was slightly lower at 47.87. By comparison, the average admission rate for PHS was 26.67 per
provider per year, 21.2 fewer annual admissions than MMA (P = .002, 95% confidence interval
[CI] = -10.3, -32.7) and 24.8 fewer admissions than BMA (P < .001, 95% CI = -14.0, -36.2).
10
3. Discussion
This study compared hospital admission rates from three primary care practices located in
community, Altoona, Blair County, Pennsylvania. Partnering for Health Services (PHS) is a full
service, insurance-free primary care practice serving low-income uninsured residents. Blair
Medical Associates (BMA) and Mainline Medical Associates (MMA) are the community’s two
largest traditional insurance fee-for-service primary care practices serving approximately 60% of
privately insured patients and 5% Medicaid patients. Data on patients’ chronic illnesses,
household income, number of provider visits, and number of hospitalizations were used to
compare among the three practices patients chronic health conditions, household income,
patient-physician visits, and hospitalization rates over the three year period, 2009-2011. Of the
three practices examined, the PHS clinic recorded the highest number of patient office visits
(nearly twice that of BMA and MMA) and the lowest number of hospital admissions (nearly half
that of BMA and MMA).
One possible explanation for PHS’s visits/hospitalizations numbers is the clinic’s
insurance administration-free model, reducing insurance-related access problems for qualified
low-income patients as well as eliminating the time physicians and nurses must allocate toward
administering health insurance. Recent research suggests the time burden associated with
insurance administration could be substantial [2,3,18]. According to Michelle Adams, Clinical
Director of Partnering for Health Services, based on her past experience in a traditional insurance
fee-for-service office:
“When a patient has any type of insurance there are always numerous steps the provider
and office staff must take in order to ensure payment of the service for the patient. These steps
can be very time consuming and are usually spent on the phone with an insurance representative
anywhere from 30 to 60 minutes for the approval of one test for one patient. Of course, this step
11
is repeated multiple times each day, which consequently leads to less time directly spent on
patient care and education.”
Reflecting on her current role at the PHS clinic, Ms Adams remarks:
“…fortunately, PHS provides office visits, ancillary testing, hospital admissions,
emergency department visits, and, in most cases, consultations with specialists at low or no cost.
The PHS design not only greatly benefits patients from a financial perspective but also benefits
them from a healthcare delivery perspective. All our staff including the clerical staff, nurses and
providers have increased time to spend on direct patient care.” (Michelle Adams, PHS, personal
communication, August 19, 2013).
Our results are consistent with some prior research in which greater patient-physician
contact lead to improvements in patients’ health outcomes [10-13,19-21]. Whether this
increased contact improves patients’ ability to better follow treatment guidelines and/or
encourages patient activation (a patient’s willingness and ability to take independent action to
manage their health and care [22]), evidence from this research found that, even after disease
severity and demographic characteristics were controlled for, patients’ active engagement with
their healthcare providers resulted in lower rates of costly use of medical services such as
hospitalizations and emergency department visits. Albeit limited, there is also research
suggesting that patient engagement strategies applied to uninsured patients in a low or no cost
medical clinic setting results in significant improvements in patients’ management of their
chronic diseases [23-25]. The treatment and patient activation strategies examined in these prior
studies, which included a nurse-managed delivery system, evidence-based disease management
guidelines, and promotion of patient self-management, are very similar to the PHS model. The
PHS model is designed to aggressively manage chronic health conditions of the uninsured by
moving them from the outpatient setting to the inpatient setting. By design, the clinic provides
more face time with physicians, physician’s assistants, and nurses to provide care for chronic
illnesses. For example, by request of the clinic, diabetic patients are seen at PHS as often as
once or twice per week until control over their diabetes is established. COPD patients are also
12
encouraged to visit the clinic as early as possible upon onset of an exacerbation in order for the
clinic to monitor changes in their condition (see e.g., Lawlor et al. [26]). This approach would
be difficult for BMA and MMA to implement since insurance generates a variable cost with each
office visit due to the tasks of billing and processing co-pays and deductibles. Indeed, the
current U.S. health insurance market discourages frequent use of primary care services and
would dramatically increase premiums if every chronically ill patient would utilize services in
this manner [2, 3, 9]. The PHS’s cost is fixed; this cost does not change by increasing or
decreasing office visits because each patient’s visit does not generate a bill that must be
processed.
The PHS clinic also utilizes a “nurse-closer” whose role is to review test results, explain
and reinforce the treatment information provided by the physician, and explain and call in
prescriptions, thus strengthening the PHS clinic’s aggressive approach to managing chronic
conditions. According to the CEO’s of BMA and MMA [personal communications, Val
Mignogia, CEO, MMA; David Duncan, CEO, BMA], since the nursing staff spends nearly half
their clinical time processing insurance, both practices would have to increase their staff to make
available a nurse-closer to meet exclusively with every patient at discharge. Such an increase in
staff would make it difficult for the practices to maintain neutral operating margins.
Although the Patient Protection and Affordable Care Act (PPACA), when implemented
in full, projects 32 million additional Americans will acquire health insurance coverage, the
Congressional Budget Office estimates that 23 million people will remain uninsured. There will
also continue to be a large number of immigrants and others without access to insurance. Since
most of our Nation’s uninsured are low-income working households, their healthcare options are
limited even if they qualify for Medicaid. As a result, the uninsured often delay or forego
13
necessary primary and preventive healthcare due to cost and/or access problems. Most policy
makers, healthcare industry leaders, and healthcare providers agree that accessible primary care
for the uninsured can be long-run cost saving as early and preventive care costs less than use of
emergency department or inpatient services that might later be needed for undertreated chronic
health conditions. As such, communities, hospitals, and other healthcare providers will need to
continue exploring new mechanisms for providing primary care services to vulnerable
populations. The results of this study suggest that access to primary care could help reduce the
inefficient use of hospital resources for non-urgent care for the uninsured and thus reduce costly
hospitalizations even in the short-run.
In August 2013, the Pennsylvania General Assembly unanimously passed Senate Bill 5
(SB5), which makes available $10 million annually to the State Department of Health for grants
to hospital-based clinics [27]. The $10 million appropriation is sufficient to fund a pilot program
to open 5 new clinics that replicate the PHS model of primary care delivery. Funding will cover
all salaries, operating and laboratory costs for a clinic of up to 1500 patients, as well as
additional money to expand the clinics’ range of care to improve prenatal, obstetric, postpartum
and newborn care. In addition, another $5 million annually will be made available as tax credits
to businesses that donate funds, products, or services to a hospital-based health care clinic.
SB5’s design was based in part on the record of success of the PHS clinic. For example, from
2009 to 2012, Altoona Regional Health System funded the PHS clinic’s operating costs and
salaries at an average annual cost of $1.36 million. On average, Altoona Regional realized an
estimated annual savings of $1.49 million in avoided emergency department visits and inpatient
admissions from uninsured patients, for a net annual savings of $201,414 [28].
14
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... Another strategy for improving access to urgent primary care is distributing some treatments to local outpatient treatment facilities (Chestnutt et al., 2009). This integrated shared professional responsibility may maximize capacity to provide urgent primary health care to all patients (Link et al., 2014) while reducing the number of higher cost hospitalizations (Agee and Gates, 2014). Regional clinics and smaller local treatment facilities in less affluent areas providing this care may also better address the society's needs to manage acute infections (Christensen et al., 2012). ...
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Objectives: To examine the distribution of treatment facilities accepting patients with acute odontogenic maxillofacial infections (AOMIs), time trends in incidence and relate these infections with a number of determinants. Methods: A national Lithuanian retrospective study gathered data on all patients treated in outpatient/inpatient treatment facilities. Adjusted Incidence Ratios (AIRs) of AOMIs were calculated separately for each type of infection and for each year. Administrative districts (ADs) were grouped into low, medium, and high thirds based on the regional determinants: socio-economic index (R-SEI), access to basic (R-BDCI) or specialized dental care (R-SDCI) and index of systemic diseases (R-ISD). Results: There were no statistically significant geographical differences in the distribution of TFs providing care for patients with AOMIs. Numbers of treatment facilities consistently increased from 2009 to 2013, but there was no consistent increase/ decrease in the incidence of AOMIs (~1%). Regions with the highest R-SEI tended to have a higher incidence of AOMIs as compared to regions with medium or low R-SEI. When controlled for other determinants, lower R-BDCI∕R-SDCI scores were associated with a higher incidence of AOMIs. Conclusions: High annual incidences (~1% of a total population) were diagnosed and treated for AOMIs, but there was no consistent time trend for these infections.
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Background: Changes to the method of payment for healthcare providers, including pay-for-performance schemes, are increasingly being used by governments, health insurers, and employers to help align financial incentives with health system goals. In this review we focused on changes to the method and level of payment for all types of healthcare providers in outpatient healthcare settings. Outpatient healthcare settings, broadly defined as 'out of hospital' care including primary care, are important for health systems in reducing the use of more expensive hospital services. Objectives: To assess the impact of different payment methods for healthcare providers working in outpatient healthcare settings on the quantity and quality of health service provision, patient outcomes, healthcare provider outcomes, cost of service provision, and adverse effects. Search methods: We searched CENTRAL, MEDLINE, Embase (searched 5 March 2019), and several other databases. In addition, we searched clinical trials platforms, grey literature, screened reference lists of included studies, did a cited reference search for included studies, and contacted study authors to identify additional studies. We screened records from an updated search in August 2020, with any potentially relevant studies categorised as awaiting classification. Selection criteria: Randomised trials, non-randomised trials, controlled before-after studies, interrupted time series, and repeated measures studies that compared different payment methods for healthcare providers working in outpatient care settings. Data collection and analysis: We used standard methodological procedures expected by Cochrane. We conducted a structured synthesis. We first categorised the payment methods comparisons and outcomes, and then described the effects of different types of payment methods on different outcome categories. Where feasible, we used meta-analysis to synthesise the effects of payment interventions under the same category. Where it was not possible to perform meta-analysis, we have reported means/medians and full ranges of the available point estimates. We have reported the risk ratio (RR) for dichotomous outcomes and the relative difference (as per cent change or mean difference (MD)) for continuous outcomes. Main results: We included 27 studies in the review: 12 randomised trials, 13 controlled before-and-after studies, one interrupted time series, and one repeated measure study. Most healthcare providers were primary care physicians. Most of the payment methods were implemented by health insurance schemes in high-income countries, with only one study from a low- or middle-income country. The included studies were categorised into four groups based on comparisons of different payment methods. (1) Pay for performance (P4P) plus existing payment methods compared with existing payment methods for healthcare providers working in outpatient healthcare settings P4P incentives probably improve child immunisation status (RR 1.27, 95% confidence interval (CI) 1.19 to 1.36; 3760 patients; moderate-certainty evidence) and may slightly increase the number of patients who are asked more detailed questions on their disease by their pharmacist (MD 1.24, 95% CI 0.93 to 1.54; 454 patients; low-certainty evidence). P4P may slightly improve primary care physicians' prescribing of guideline-recommended antihypertensive medicines compared with an existing payment method (RR 1.07, 95% CI 1.02 to 1.12; 362 patients; low-certainty evidence). We are uncertain about the effects of extra P4P incentives on mean blood pressure reduction for patients and costs for providing services compared with an existing payment method (very low-certainty evidence). Outcomes related to workload or other health professional outcomes were not reported in the included studies. One randomised trial found that compared to the control group, the performance of incentivised professionals was not sustained after the P4P intervention had ended. (2) Fee for service (FFS) compared with existing payment methods for healthcare providers working in outpatient healthcare settings We are uncertain about the effect of FFS on the quantity of health services delivered (outpatient visits and hospitalisations), patient health outcomes, and total drugs cost compared to an existing payment method due to very low-certainty evidence. The quality of service provision and health professional outcomes were not reported in the included studies. One randomised trial reported that physicians paid via FFS may see more well patients than salaried physicians (low-certainty evidence), possibly implying that more unnecessary services were delivered through FFS. (3) FFS mixed with existing payment methods compared with existing payment methods for healthcare providers working in outpatient healthcare settings FFS mixed payment method may increase the quantity of health services provided compared with an existing payment method (RR 1.37, 95% CI 1.07 to 1.76; low-certainty evidence). We are uncertain about the effect of FFS mixed payment on quality of services provided, patient health outcomes, and health professional outcomes compared with an existing payment method due to very low-certainty evidence. Cost outcomes and adverse effects were not reported in the included studies. (4) Enhanced FFS compared with FFS for healthcare providers working in outpatient healthcare settings Enhanced FFS (higher FFS payment) probably increases child immunisation rates (RR 1.25, 95% CI 1.06 to 1.48; moderate-certainty evidence). We are uncertain whether higher FFS payment results in more primary care visits and about the effect of enhanced FFS on the net expenditure per year on covered children with regular FFS (very low-certainty evidence). Quality of service provision, patient outcomes, health professional outcomes, and adverse effects were not reported in the included studies. Authors' conclusions: For healthcare providers working in outpatient healthcare settings, P4P or an increase in FFS payment level probably increases the quantity of health service provision (moderate-certainty evidence), and P4P may slightly improve the quality of service provision for targeted conditions (low-certainty evidence). The effects of changes in payment methods on health outcomes is uncertain due to very low-certainty evidence. Information to explore the influence of specific payment method design features, such as the size of incentives and type of performance measures, was insufficient. Furthermore, due to limited and very low-certainty evidence, it is uncertain if changing payment models without including additional funding for professionals would have similar effects. There is a need for further well-conducted research on payment methods for healthcare providers working in outpatient healthcare settings in low- and middle-income countries; more studies comparing the impacts of different designs of the same payment method; and studies that consider the unintended consequences of payment interventions.
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This study assesses the cost-effectiveness of an insurance administration-free, hospital-based clinic designed to provide a full array of primary care services to low-income individuals at little or no cost. In addition to low/no-cost visits, individuals have the option to purchase a low-cost health insurance plan similar to any traditional health plan (eg, prescriptions, primary care, specialty care, durable medical equipment, radiology, laboratory test results). We used 3 years of data (2009-2012) on emergency department (ED) visits and inpatient hospital admissions from clinic patients and patients at the community’s 2 largest private physician groups to assess the cost-effectiveness of the hospital-based clinic in terms of ED and inpatient admission costs avoided and financial sustainability of the low-cost insurance plan. Estimated annual savings in hospital inpatient and ED costs were approximately 1.4 million. Insurance plan data indicated sound fiscal sustainability with modest provider reimbursement growth and zero annual premium growth.
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Access to primary care could reduce use of more costly health care by uninsured individuals through prevention and early treatment. We analyzed data from a program providing free primary care to test this hypothesis. We compared emergency room (ER) visits and hospitalizations among uninsured, low-income adults who received immediate versus delayed access to a program providing free primary care, including labs, X-rays, and specialty consultation. We used surveys to identify ER visits and hospitalizations during the 12 months preceding and following program enrollment or wait list entry. Hospitalizations decreased from the year before entry to the year following entry in participants with immediate and delayed (6.0% vs 8.8% decrease) access. ER use also decreased in both groups (11.2% vs 15.4%). Free primary care services and specialty consultation did not reduce use of more costly health care services during its first year. More prolonged availability of primary care might have greater impact.
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Patient engagement is an increasingly important component of strategies to reform health care. In this article we review the available evidence of the contribution that patient activation-the skills and confidence that equip patients to become actively engaged in their health care-makes to health outcomes, costs, and patient experience. There is a growing body of evidence showing that patients who are more activated have better health outcomes and care experiences, but there is limited evidence to date about the impact on costs. Emerging evidence indicates that interventions that tailor support to the individual's level of activation, and that build skills and confidence, are effective in increasing patient activation. Furthermore, patients who start at the lowest activation levels tend to increase the most. We conclude that policies and interventions aimed at strengthening patients' role in managing their health care can contribute to improved outcomes and that patient activation can-and should-be measured as an intermediate outcome of care that is linked to improved outcomes.
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Purpose: We used the Surveillance Epidemiology and End Results (SEER)-Medicare database to explore the association between primary care and breast cancer outcomes. Methods: Using a retrospective cohort study of 105,105 female Medicare beneficiaries with a diagnosis of breast cancer in SEER registries during the years 1994-2005, we examined the total number of office visits to primary care physicians and non-primary care physicians in a 24-month period before cancer diagnosis. For women with invasive cancers, we examined the odds of diagnosis of late-stage disease, according to the American Joint Commission on Cancer (AJCC) (stages III and IV vs stages I and II), and survival (breast cancer specific and all cause) using logistic regression and proportional hazards models, respectively. We also explored whether including noninvasive cancers, such as ductal carcinoma in situ (DCIS), would alter results and whether prior mammography was a potential mediator of associations. Results: Primary care physician visits were associated with improved breast cancer outcomes, including greater use of mammography, reduced odds of late-stage diagnosis, and lower breast cancer and overall mortality. Prior mammography (and resultant earlier stage diagnosis) mediated these associations in part, but not completely. Similar results were seen for non-primary care physician visits. Results were similar when women with DCIS were included in the analysis. Conclusions: Medicare beneficiaries with breast cancer had better outcomes if they made greater use of a primary care physician's ambulatory services. These findings suggest adequate primary medical care may be an important factor in achieving optimal breast cancer outcomes.
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This study estimates the benefits and costs of a free clinic providing primary care services. Using matched data from a free clinic and its corresponding regional hospital on a sample of newly enrolled clinic patients, patients' non-urgent emergency department (ED) and inpatient hospital costs in the year prior to clinic enrollment were compared to those in the year following enrollment to obtain financial benefits. We compare these to annual estimates of the costs associated with the delivery of primary care to these patients. For our sample (n = 207), the annual non-urgent ED and inpatient costs at the hospital fell by $170 per patient after clinic enrollment. However, the cost associated with delivering primary care in the first year after clinic enrollment cost $505 per patient. The presence of a free primary care clinic reduces hospital costs associated with non-urgent ED use and inpatient care. These reductions in costs need to be sustained for at least 3 years to offset the costs associated with the initially high diagnostic and treatment costs involved in the delivery of primary care to an uninsured population.
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Chronic obstructive pulmonary disease (COPD) causes substantial morbidity and mortality and may be unrecognized in its early stages. Chronic lower respiratory disease (CLRD), which includes both COPD and asthma, was the third leading cause of death in the United States in 2008. COPD includes chronic bronchitis and emphysema, which both make emptying air from the lungs progressively more difficult and can be associated with cough, mucus production, wheezing, and breathlessness. Risk factors include primarily cigarette smoking, but also exposure to noxious particles or gases, recurrent infection, diet, and genetic factors. COPD is often preventable, but there is no cure. Treatment can control symptoms and slow disease progression. This report presents trends in COPD prevalence, hospitalization, and death rates, and detailed recent estimates for population subgroups. Asthma is excluded from this report because it is considered a different condition with fully reversible symptoms, although some people may have asthma and COPD concurrently.
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Background For chronically ill patients, readmission to the hospital can be frequent and costly. We studied the effect of an intervention designed to increase access to primary care after discharge from the hospital, with the goals of reducing readmissions and emergency department visits and increasing patients' quality of life and satisfaction with care. Methods In a multicenter randomized, controlled trial at nine Veterans Affairs Medical Centers, we randomly assigned 1396 veterans hospitalized with diabetes, chronic obstructive pulmonary disease, or congestive heart failure to receive either usual care or an intensive primary care intervention. The intervention involved close follow-up by a nurse and a primary care physician, beginning before discharge and continuing for the next six months. Results The patients were severely ill. Half of those with congestive heart failure (504 patients) had disease in New York Heart Association class III or IV; 30 percent of those with diabetes (751 patients) had end-organ damage; and a quarter of those with chronic obstructive pulmonary disease (583 patients) required home oxygen treatment or oral corticosteroids. The patients had extremely poor quality-of-life scores. Although they received more intensive primary care than the controls, the patients in the intervention group had significantly higher rates of readmission (0.19 vs. 0.14 per month, P = 0.005) and more days of rehospitalization (10.2 vs. 8.8, P = 0.041). The patients in the intervention group were more satisfied with their care (P<0.001), but there was no difference between the study groups in quality-of-life scores, which remained very low (P = 0.53). Conclusions For veterans discharged from Veterans Affairs hospitals, the primary care intervention we studied increased rather than decreased the rate of rehospitalization, although patients in the intervention group were more satisfied with their care.
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Current primary care practice under a dysfunctional fee-for-service payment system has been likened to being on a “hamster wheel,” where many physicians find that the only way to make ends meet is to increase visit volume and perhaps add a generously reimbursed procedure (eg, skin biopsy or ultrasonography) to the office visit. As one of my patients said, “You’d get paid much more if you cut my toenails while talking to me.” Patients are unhappy, physicians are demoralized, office staff are frazzled, and applications to primary care residencies have dropped to all-time lows.1- 3