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 20: Big Brother is Watching: Utilizing Clinical Decision Support as a Tool to Limit Adverse Drug Events (ATTACHED)
  • Then, do the following:

  • Conduct research on “community health centers” by reviewing the National Association of Community Health Centers website at About Our Health Centers – NACHC (
  • Choose a geographic region or location and research the types of services delivered at the community health centers in that area.
  • Describe the location including the demographics such as race and ethnicity, gender, socio-economic status, age, education level, morbidity and mortality statistics, and other common or relevant health statistics.
  • Identify the other social services that are available to your selected community to strengthen and sustain the community health system in the location you have chosen.
  • Identify and discuss the barriers to access to healthcare.
  • Explain how healthcare providers can improve access to quality care and reach their target population.
  • CASE ​20Big Brother Is Watching:
    Utilizing Clinical Decision Support as a Tool to Limit
    Adverse Drug Events
    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.
    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.
    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 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.
    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
    • Performing pulmonary function tests
    • Giving influenza and pneumococcal vaccinations

    Prescribing inhaled steroid preparations in patients with frequent symptoms of

    Prescribing inhaled anticholinergic agents in patients with chronic obstructive
    pulmonary disease
    • Escalating doses of inhaled b-adrenergic agonists for all patients with persistent
    • 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):
    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
    Alert Category
    Drugs listed on the Beers list of medications not
    designed for the elderly population
    Drugs that have a less expensive generic
    Drugs that have a more effective alternative listed
    on clinical initiatives (created by long-term care
    pharmacy expert panel)
    Drugs for short-term or acute use
    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?
    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
    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
    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.
    1​. Institute of Medicine. ​To Err Is Human: Building a Safer Health System​. Kohn LT, Corrigan JM,
    Donaldson MS, eds. Washington, DC: National Academy Press; 2000.
    2​. Hazell L, Shakir SA. Under-reporting of adverse drug reactions: a systematic review. ​Drug Saf.​
    3​. Aitken M, Berndt E, Cutler D. Prescription drug spending trends in the United States: looking
    beyond the turning point. ​Health Aff​. 2009;28(1):w151–w160.
    4​. Cannon KT, Choi MM, Zuniga MA. Potentially inappropriate medication use in elderly patients
    receiving home health care: a retrospective data analysis. ​Am J Geriatr Pharmacother.​
    5​. Ernst FR, Grizzle AJ. Drug-related morbidity and mortality: updating the cost-of-illness model.
    J Am Pharm Assoc (Wash)​. 2001;41:192–199.
    6​. Page RL II, Ruscin F. The risk of adverse drug events and hospital-related morbidity and
    mortality among older adults with potentially inappropriate medication use. ​Am J Geriatr
    Pharmacother.​ 2006;4(4):297–305.
    7​. Beyth R, Shorr R. Epidemiology of adverse drug reactions in the elderly by drug class. ​Drugs
    Aging​. 1999;14(3):231–239.
    8​. Budnitz D, Pollock D, Weidenbach K, Mendelsohn A, Schroeder T, Annest J. National
    surveillance of emergency department visits for outpatient adverse drug events. ​JAMA.​
    9​. Chester M, Chen L, Kaski JC. Identification of patients at high risk for adverse coronary events
    while awaiting routine coronary angioplasty. ​Br Heart J.​ 1995;73:216–222.
    10​. Gurwitz JH, Field TS, Avorn J, et al. Incidence and preventability of adverse drug events in
    nursing homes. ​Am J Med​. 2000;109:87–94.
    11​. Eric T, David S, Burstin H, et al. Incidence and types of adverse events and negligent care in
    Utah and Colorado. ​Medical Care​. 2000;38(3):261–271.
    12​. Wilt TJ, Bloomfield HE, MacDonald R, et al. Effectiveness of statin therapy in adults with
    coronary heart disease. ​Arch Intern Med.​ 2004;164:1427–1436.
    13​. van der Harst P, Voors AA, van Gilst WH, et al. Statins in the treatment of chronic heart
    failure: biological and clinical considerations. ​Cardiovasc Res​. 2006;71(3): 443–454.
    14​. Ma J, Sehgal NL, Ayanian JZ, et al. (2005) National trends in statin use by coronary heart
    disease risk category. ​PLoS Med.​ 2005;2(5):e123.
    15​. Hutchins F, Unger J, Crowley J, Coltman C, Albain K. Underrepresentation of patients age 65
    years and older in cancer-treatment trials. ​N Engl J Med​. 1999;341:2061–2067.
    16​. Gurwitz J, Field TS, Harrold LR, et al. Incidence and preventability of adverse drug events
    among older persons in the ambulatory setting. ​JAMA​. 2003;289:1107–1116.
    17​. Staroselsky M, Volk LA, Tsurikova R, et al. (2008). An effort to improve electronic health
    record list accuracy between visits: patients’ and physicians’ response. ​Int J Med Inf​.
    18​. Fick DM, Cooper JW, Wade WE, et al. Updating the Beers criteria for potentially
    inappropriate medication use in older adults: results of a U.S. consensus panel of experts. ​Arch
    Intern Med​. 2003;163:2716–2724. [Published correction appears in ​Arch Intern Med.​ 2004;164:298].
    19​. Chase BA, Bennett NL. Medication safety, systems and communications.
    ourseInformation/default.htm​. Published 2006. Accessed September 25, 2010.
    20​. Williams AF, Manias E, Walker R. Adherence to multiple, prescribed medications in diabetic
    kidney disease: a qualitative study of consumers’ and health professionals’ perspectives. ​Int J Nurs
    Stud​. 2008;45(12):1742–1756.
    21​. Handler SM, Nace DA, Studenski SA, Fridsma DB. Medication error reporting in long term
    care. ​Am J Geriatr Pharmacother​. 2004;2:190–196.
    22​. Morgan DG, Semchuk KM, Stewart NJ, D’Arcy C. Job strain among staff of rural nursing
    homes: a comparison of nurses, aides, and activity workers. ​J Nurs Adm​. 2002;32(3):152.
    23​. Crespin DJ, Modi AV, Wei D, et al. Repeat medication errors in nursing homes: contributing
    factors and their association with patient harm. ​Am J Geriatr Pharmacother​. 2010;8(3):258–270.
    24​. Aparasu RR, Mort JR, Brandt H. Polypharmacy trends in office visits by the elderly in the
    United States, 1990 and 2000. ​Res Soc Adm Pharm.​ 2005;1(3):446–459.
    25​. Lococo KH, Staplin L. ​Literature Review of Polypharmacy and Older Drivers: Identifying
    Strategies to Collect Drug Usage and Driving Functioning among Older Drivers​. Washington, DC:
    U.S. Department of Transportation, National Highway Traffic Safety Administration; 2006. Report no.
    DOT HS 810 558.
    26​. Moylan KC, Binder EF. Falls in older adults: risk assessment, management and prevention.
    Am J Med.​ 2007;120(6):493.e1–493.e6.
    27​. Allen D, Fenwick M. Continued progress: hospital use of information technology, ​American
    Published 2007. Accessed October 14, 2010.
    28​. Burt CW, Hing E. Use of computerized clinical support systems in medical settings: United
    States, 2001–03. ​Advance Data​. 2005;353:1–8.
    29​. Wager K, Schaffner M, Foulois B, Swanson Kazley A, Parker C, Walo H. Comparison of the
    quality and timeliness of vital signs data using three different data-entry devices. ​Comput Inform
    Nurs.​ 2010;28(4):205–212.
    30​. Johnston ME, Langton KB, Haynes RB, Mathieu A. Effects of computer-based clinical
    decision support systems on clinician performance and patient outcome. A critical appraisal of
    research. ​Ann Intern Med.​ 1994;120(2):135–142.
    31​. Smith DH, Perrin N, Feldstein A, et al. The impact of prescribing safety alerts for elderly
    persons in an electronic medical record: an interrupted time series evaluation. ​Arch Intern Med.​
    32​. 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.
    33​. 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.

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