Community Health Centers Paper

Access to Healthcare

Community health centers provide primary health care services to more than 22 million people across the United States. Each health center provides for the unique needs of patients in its immediate community. Many of the patients that are seen at health centers represent uninsured or underinsured patients and/or those with limited access to healthcare. This assignment will help you identify service delivery needs and provide quality recommendations for improving access to healthcare.

Read the following case study from your textbook:

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CASE ​20Big Brother Is Watching:
Utilizing Clinical Decision Support as a Tool to Limit
Adverse Drug Events
AARON ROBERTS
A CRISIS IN THE EMERGENCY ROOM
It was nearing 3 p.m. in the emergency department and the charge nurse was
rapidly losing her daily battle to make the hands of the clock move more quickly. The
day had proven to be somewhat busy for a Friday afternoon, and she couldn’t wait
for a chance to rest her weary feet. The most disappointing case of the day involved
a kind, elderly gentleman that the attending physician was only barely able to
stabilize. She remembered taking a peek at his chart and being stunned at how
quickly a manageable situation had gotten out of hand. A little past 8 a.m., “Miller,
Richard” had arrived in her unit after his routine morning finger prick had alerted him
to an increased blood glucose level—high enough, he thought, to warrant some
medical attention. The 78-year-old patient had recounted his current medication to
the receptionist and the staff had checked multiple times to determine whether he
had any drug allergies. After a brief stint in the waiting area and a decently short
time in one of the emergency beds, he was out the door. In hand were two new
prescriptions and an extra dose of comfort. However, 4 hours later, he was back in
the emergency department, this time in critical condition with clear signs of an
adverse drug reaction. The charge nurse could only wonder whether direct hospital
error or some other cause had contributed to Mr. Miller’s condition.
MEDICATION ERRORS
In the United States, medication errors account for 44,000 deaths and millions of
hospitalizations and clinical visits a year.​1 These statistics are the consequences of
adverse drug events (ADEs), which are any unexpected side effects attributed to the
use of a combination of drugs. Researchers have estimated that 95% of adverse drug
reactions go unreported.​2 However, of those ADEs that are recognized, a substantial
proportion can be attributed directly to mistakes made by hospital staff. Many health
providers have turned to new health information technology as a way to reduce this
burgeoning problem. In particular, the development of computer-decision support
systems is a potential step toward reducing this problem and a step toward preventing
people like Richard Miller from spending his night in the emergency department.
Costs
In 2003, U.S. spending on prescription drugs soared to over $200 billion, with spending
for 40 popular drugs reaching $1 billion apiece.​3 The prevalence of suboptimal
prescribing practices has been estimated to be as high as 30%.​4 Every year, $177.4
billion is wasted to cover morbidity and mortality caused by adverse drug events.​5 A
drug reaction may lead to hospitalization and outpatient treatment, which can account
for over a third of hospital visits for the aging population.​6 When combined, the direct
costs for hospitalization for ADEs, the costs of poor prescribing practices, and
calculated indirect costs amount to a sum that would reinvigorate an ailing U.S.
healthcare system.
Health Disparities
Mr. Miller, as an elderly diabetic, belongs to a subgroup that experiences a
disproportionate number of adverse drug events. Although increased age has been
cited as a key risk factor for adverse drug reactions, other measures, such as the
existence of comorbidities, better typify this issue.​7 Among older patients, the drugs
insulin, warfarin, and digoxin contribute to a third of all adverse drug events.​8 Patients
with chronic conditions, especially those that affect drug distribution, experience more
adverse drug events.​9 Individuals taking anticoagulants or medications that interfere
with the central nervous system are also at increased risk.​10 Additionally, whites and
those with private insurance are more likely to face adverse drug reactions as their
increased access to health services causes them to be at risk more often.​11
Limited Research
The rush of new and increasingly popular drugs to the market has led to controversy
regarding the effectiveness and appropriateness of certain treatment options. For
example, statins, a class of drugs designed to reduce levels of low-density lipoprotein
cholesterol, are widely prescribed. Every year, articles show strong evidence that these
drugs are extremely effective at producing better health outcomes.​12 They are countered
by equally striking evidence showing this class of drugs is largely ineffective.​13 The
uncertainty demonstrated by scientific research hasn’t slowed the increase in
prescribing rates, with statins quadrupling in total use among ambulatory patients over
10 years and occupying 90% of the lipid-lowering market in 2002.​14 Researchers are
unlikely to use older patients as subjects in trials to test new drugs.​15 So, one possibility
in Mr. Miller’s case is that the hospital staff may have administered the correctly
indicated drug, but the research that contributed to its approval may not have been
comprehensive. Additionally, physicians don’t generally wait for the final verdict on
effectiveness before choosing to prescribe new drugs, making adverse drug events and
suboptimal prescribing a possibility.
FIGURE 20-1​ Medication appropriateness index.
Source: Hanlon JT, Schmader KE, Samsa GP, et al. A method for assessing drug therapy
appropriateness. ​J Clin Epidemiol​. 1992;45:1045–1051.
Prescribing Errors
On the other hand, the physician in charge of Mr. Miller’s case may have prescribed an
incorrect medication—either one that negatively impacted his concurrent ailments or
one that interacts poorly with other medications. Patients often see a variety of different
specialists, who aren’t always given accurate information about patient medication
history.​16 Changes to drug regimens made by one provider may not always be relayed
to other physicians caring for a shared patient.​17 Even when such prescribing
information is shared, physicians and pharmacists may have limited knowledge about
adverse drug events because they are not up to date on current findings. To limit
prescribing errors, physicians should be using tools like the Beers criteria for potentially
inappropriate medication use in the elderly​18 and the Medication Appropriateness Index
(see ​Figure 20-1​) to limit prescribing errors.
Mistakes can occur elsewhere as well. Appropriately prescribed medication may not
reach the patient due to poor handwriting or overuse of abbreviations. Telephone orders
may be easily confused.​19 When healthcare providers overcome these barriers, patients
are able to get the correct prescriptions and will hopefully be placed along the path
toward improved health.
Medication Adherence
It’s possible that Mr. Miller may have taken an incorrect dosage or combined his new
prescription with some synergistic medium such as alcohol. Medication adherence
requires proper understanding of proper dosage and when to take medications as well
as a willingness to stick to drug regimens. Simple things, like remembering to take
medication, also become more difficult as people age or succumb to disorders like
dementia. The costs associated with many popular pharmaceuticals can also hinder
medication adherence, as patients sometimes choose not to fill prescriptions in order to
save money.
Strong provider-patient relationships, in which patients have their concerns
answered, contribute to higher rates of adherence.​14 Patients take the medications they
feel were prescribed correctly and will achieve the desired results. Assistance from
physicians and pharmacists with prescription refills and medication schedules helps to
improve patient comprehension and increase medication adherence. The minimal time
available to hurried emergency physicians and nurses may not leave sufficient time to
ensure patients like Mr. Miller understand their medication instructions.​20
Challenges in Long-Term Care
Nursing homes continue to care for a substantial fraction of the population over 65.
Some would argue Mr. Miller may need more directed care if he cannot manage his
medication needs. Unfortunately, the choice to move an elderly patient to managed care
doesn’t completely protect him from adverse drug events. Studies have shown that
nursing home patients take, on average, 8.8 medications daily, making nursing homes
breeding grounds for medication error.​21 Patients have been found to receive the
incorrect medication, to receive medication at the wrong time of day, or miss a
treatment altogether. Nursing home workers have a high level of job strain, have to
cope with poor staffing levels, and often feel guilty about not being able to adequately
meet the needs of residents.​22 Within such a high-demand work environment, mistakes
are not surprising. In a third of cases, however, medication errors are made
repeatedly.​23 Decreasing rates of adverse drug events not only provides better health
outcomes among nursing home residents; it also helps to lower the already stifling costs
of institutionalized care.
Polypharmacy as a Risk Factor
Mr. Miller’s earlier trip to the emergency department and his current condition could be
completely coincidental. It is possible his body had not withstood the effects of
polypharmacy. Polypharmacy, the excessive prescription of medication, has begun to
take a heavy toll on the elderly population. By 2000, 37 million doctor visits by people
over the age of 65 (one quarter of all such visits) ended with patients being prescribed
at least five different medications.​24 Excessive prescription can refer simply to the
number of medications (usually five or more) or to cases in which drugs are
inappropriately or unnecessarily prescribed. Patients taking more than five medications
are more likely to have combinations that lead to adverse drug events.​14 The reaction
that brought Mr. Miller back into the hospital could have been caused by one of his
other combinations and may have had nothing to do with his new prescriptions.
Nonetheless, the most effective approach to prevent his unfortunate situation would
have been if the emergency department staff had taken steps to reassess his drug use.
ADVERSE DRUG EVENTS AS A PUBLIC HEALTH CONCERN
Adverse drug events are a concern for individuals as well as communities. Driving has
been an area of particular concern for older adults. Many medications, especially
antidepressants, alter the ability of an individual to stay focused behind the wheel. Very
little research has been conducted on the effect of multiple medications on driving
ability.​25 Considering the general decline in driving ability and reaction time associated
with specific classes of drugs, this should be an area of concern for policy makers. It
would have been most unfortunate if Mr. Miller had experienced his reaction while
behind the wheel.
Falls are also associated with adverse drug events. Individuals prescribed numerous
prescriptions are at high risk for falls and consequent debilitating injuries. Falls are a
serious concern for the aging population as the associated injuries can often lead to a
loss of independence or hasten individual decline toward death. Interventions designed
to reduce the incidence of falls often aim to reduce adverse combinations of drugs
among patients.​26 Patients who take fewer medications have fewer drug reactions that
interfere with their daily activities.
HEALTH INFORMATION TECHNOLOGY—A SOLUTION?
New technologies are always emerging in health-related fields as older instruments are
updated or a new product is designed to increase productivity. Electronic medical
records (EMRs) emerged as a way to improve quality of care and increase efficiency.
An EMR serves as a replacement for paper medical records by electronically storing
critical patient information in a central location.​27 EMRs provide enhanced
documentation and allow physicians to pool patient information. They may offer certain
clinical tools, like medication ordering or patient assessments. Equipped with evidence
for the effectiveness of this technology, 31% of emergency departments and 17% of
physicians’ offices had switched to EMRs in 2003.​28 Currently, 11% of hospitals have
fully implemented systems, while 66% more have partially implemented systems.​29
Although a skeptical Mr. Miller may question the safety of health information being
stored electronically, his physicians are more thankful for the clarity these systems can
provide. Early support systems were programmed to provide reminders to hospital staff,
to catch errors before they occur, or to perform basic diagnostic tasks.​30 These more
progressive programs, termed ​clinical decision support (CDS) systems,​ have been
utilized in attempts to reduce the occurrence of adverse drug events.
Three examples that follow describe research into the impact of clinical decision
support programs. Could these systems have changed the circumstance at hand and
prevented Mr. Miller’s critical health crisis?
Case 1: CDS in Prescribing Practices
Researchers in the Pacific Northwest took notice of the high prevalence of adverse
drug events nationwide and the thousands of deaths that can be attributed to
preventable mistakes. Their study design attempted to reduce ADEs through the use
of CDS. All 450,000 members of a health maintenance organization group were
included in the study in an attempt to best represent the general population of this
region. The primary care physicians, nurse practitioners, and physician’s assistants
recruited for the study were already acquainted with electronic medical record
systems, allowing them to enter patient orders (for lab tests, medication needs, and
treatment options) electronically. The quasiexperimental intervention began with a
12-month observational period designed to identify provider prescribing practices,
followed by a 27-month intervention period. Using a computerized decision support
system, researchers were able to alert providers of a preferred alternative when they
prescribed a nonpreferred drug. The criterion for a nonpreferred drug was
established as those that were not indicated for use in older patients, including a
class of long-acting benzodiazepines and tertiary amine tricyclic antidepressants.
This technology alerted physicians when they prescribed one of the suboptimal
drugs. It is important to note that even though these drugs were acceptable in
younger patients, the technology raised alerts for all patients. Thus, alerts were drug
specific and did not vary based on patient characteristics or any aspect specific to a
patient’s case. After receiving an alert, providers then had the option to change the
medication or to ignore the alert. The number of medication alerts and data on
dispensing rates were used to determine the effectiveness of the intervention.​31
Patients whose cases evoked alerts were most likely to be older (22.9 alerts per
10,000 for patients over age 65 versus 8.2 alerts in patients under 65) and female
(69.4% of elderly women but only 56.2% of elderly men triggered alerts). A 22%
decrease from the initial prescribing rate of nonpreferred medications was seen after
the first month of the intervention; this lower rate of prescription of 16.1 dispenses
per 10,000 held for the duration of the trial. The use of preferred medications also
spiked 20% after the first month of the intervention and then experienced a slight but
steady increase throughout the course of the experiment. The most dramatic
changes in prescribing pattern occurred in the elderly population. However,
prescription of preferred medications also increased among nonelderly patients, an
unexpected result for researchers. The subclasses of drugs that experienced the
largest overall change in dispensing rates in favor of preferred drug classes were
those for which physicians could see clear evidence for clinical equivalency and for
which there was a consensus on drug effectiveness.​31
Overall, the use of this new technology helped to limit the frequency of
nonpreferred medications prescribed to elderly patients. The system appeared to be
widely accepted by clinicians, as some were more likely to order the preferred
medication after receiving an alert because the system automatically filled important
parts of the prescription, thus saving the physician more time to allot to adequately
treating patients.​31
Could a system like this have helped improve Mr. Miller’s situation?
Case 2: CDS in Medication Adherence
Respiratory therapies represent an area in which patients do not always employ the
recommended treatment options. One approach designed to improve treatment
utilization is to train more knowledgeable healthcare providers who can better
communicate care management strategies to patients. One randomized intervention
study was carried by Indiana University’s medical group in an attempt to increase
treatment adherence among respiratory patients. This intervention targeted patients
seen by physicians across four hospitals with shared medical records. Seven
hundred and six patients were initially enrolled in the study and about two thirds
completed the final survey.​32
Patients were randomized into four different intervention groups according to the
physicians they saw, and were additionally randomized to obtain medication from a
single pharmacist. In the first group, both the physician and the pharmacist received
the intervention, while only the provider or the pharmacist received the intervention
in the second and third. A final comparison group received no intervention.
Patient-specific care suggestions were generated by a panel of expert clinicians and
programmed into each physician’s electronic workstations, with explanations and
references. Care suggestions fit into several key categories that can be found in
Table 20-1​. As a member of the intervention group, Mr. Miller’s physician would
have been presented with a care suggestion on her workstation when she wanted to
order new care options or review patient information. Members of the intervention
groups were required to view all suggestions, with an option to order or omit the new
care options. They were also presented with information being used in concurrent
studies, such as medication warning alerts​32 (similar to the alerts from the Pacific
Northwest intervention).
Researchers were left with somewhat less promising results than they would
have liked. At the conclusion of the trial, there was no significant difference in patient
adherence or satisfaction with providers across the experimental groups. There was
an isolated improvement in emotional quality of life for those who received
medication from a pharmacist in the intervention group. In this provider-centered
approach, designed to create improvements in adherence, physicians expressed
mixed opinions about the care suggestions. Some felt the suggest ions were helpful
and educational, while others believed they were too rigorous and infringed on
physician autonomy. Physicians in the intervention groups also experienced
significantly higher healthcare costs.​32
TABLE 20-1​ Potential Suggestions for Providers Used in Case 2—CDS in Medication
Adherence
• Performing pulmonary function tests
• Giving influenza and pneumococcal vaccinations
•
Prescribing inhaled steroid preparations in patients with frequent symptoms of
dyspnea
•
Prescribing inhaled anticholinergic agents in patients with chronic obstructive
pulmonary disease
• Escalating doses of inhaled b-adrenergic agonists for all patients with persistent
symptoms
• Prescribing theophylline for patients with chronic obstructive pulmonary disease and
continued symptoms despite aggressive use of inhaled anticholinergic agents,
b-agonists, and steroids
• Encouraging smoking cessation
Source: Data from Tierney W, Overhage M, Murray M, et al. Can computer-generated
evidence-based care suggestions enhance evidence-based management of asthma and chronic
obstructive pulmonary disease? A randomized, controlled trial. ​Health Serv Res. 2005;40(2):
477–498.
Would a system like this have helped Mr. Miller’s geriatrician convey health information
more effectively, and thereby enhance Mr. Miller’s medication adherence?
Case 3: North Carolina Initiative
The North Carolina Long-Term Care Polypharmacy Initiative used CDS to improve
patient health outcomes in the nursing home setting. If fate did push Mr. Miller into a
long-term care setting, his care would be greatly impacted by the changes enacted
by this initiative. This intervention was developed by a Medicaid case management
firm, Community Care of North Carolina, and involved both physicians and
pharmacists. The study originated from a desire to lower medication costs for
long-term care residents, but evolved into an effort to reduce polypharmacy and
adverse drug events. This intervention built upon two previous pilot studies and
began with an assessment of detailed baseline demographic and health information
for over 8000 participants.​33
Lengthy computer algorithms were used to determine five categories of
medication profiles that should be brought to the attention of the pharmacists. The
alert categories, listed in ​Table 20-2​, were formed based on drug interactions, costs,
and effectiveness. Using the alert system and Medicaid claims information, patient
medication records were reviewed and flagged for further pharmacy services. The
pharmacists would then make recommendations to providers, which, based on the
discretion of the provider, could lead to a change in drug regimens. A prospective
aspect was also added to the intervention, allowing pharmacists to take action when
they received new medication orders that warranted concern. This two-pronged
approach was designed to end a cycle of bad practice, by protecting new and
existing patients from adverse medication combinations. An initial 90-day baseline
period was followed by a 90-day intervention period and a 90-day postintervention
period. Hospitalizations, drug alerts, and costs were tracked. The targeted group
was compared with other Medicaid patients not enrolled in this program in order to
provide a viable comparison group.​31
For analysis, subjects were divided into 10 groups based on the services they
received and whether those were retroactive or prospective actions. The most
significant results were found among patients who received both prospective and
retrospective services that eventually led to drug changes. On average, patients
saved about $21 per month, which could translate to annual savings of about $2
million for everyone in the initiative. Participants were immediately less likely to
experience hospitalizations, and the reduction of drug alerts suggests better medical
outcomes over longer periods of time. The total number of medications taken
remained mostly constant throughout the study.​33
TABLE 20-2​ Description of Drug Alert Categories Used in Case 3: North Carolina
Initiative
Alert Category
Description
1
Drugs listed on the Beers list of medications not
designed for the elderly population
2
Drugs that have a less expensive generic
3
Drugs that have a more effective alternative listed
on clinical initiatives (created by long-term care
pharmacy expert panel)
4
Drugs for short-term or acute use
5
Drugs that had detrimental effect due to their
metabolic processing
Source: Data from Trygstad TK, Christensen DB, Wegner SE, et al. Analysis of the North Carolina
Long-Term Care Polypharmacy Initiative: a multiple-cohort approach using propensity-score
matching for both evaluation and targeting. ​Clin Ther.​ 2009;31(9):2018–2037.
If this system had been in place in the pharmacy where Mr. Miller had filled his
prescriptions, what reviews of his existing and new medications might have been
performed? To what effect?
A BRIGHTER TOMORROW WITH BIG BROTHER
All three of these studies illustrate interventions that attempt to address the myriad
possible underlying causes of Mr. Miller’s adverse drug event. The results found in the
Pacific Northwest intervention are not atypical. Decision support systems in the clinical
field tend to be met with little resistance by clinicians and produce moderate results.
However, research also demonstrates that it’s more challenging to deal with patients
already receiving inappropriate medications. While CDS can often result in fewer
prescribing errors, it may not reach those patients who are on stable medication
regimens.
Although case 2 could ultimately prove to be a beneficial model, computer-decision
support was unable to produce positive results in this attempt to address adherence
with respiratory medications. Perhaps certain aspects of the case design or target
population prevented researchers from reaching the target goals. The North Carolina
Initiative produced much more promising results, yet it still had a few downsides to
overcome.
It is also helpful to consider what happens to prescribing practices once intervention
programs end. Once support systems are removed, have providers learned to order the
correct drugs or will they simply revert to old mistakes? Making successful interventions
sustainable is an important next step.
As healthcare systems implement these sorts of clinical decision support tools, the
innovations will be largely invisible to the patients. Mr. Miller would never directly
interact with the health information technology and would not be aware that Big Brother
was watching. All he would experience is better health.
About the Author
Aaron Roberts grew up in Rochester, New York, and recently graduated from Brown
University with a double concentration in human biology and community health. He
developed his love for medicine at quite a young age. After working as a surgical
technician in a women’s care unit, he developed an interest in the health of vulnerable
populations. Currently, his career focus is on outreach to urban youths and producing
sustainable elder care while he works his way toward medical school and an eventual
career in pediatrics.
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