Managing and Monitoring Pediatric Pain with a Mobile App

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Pain management is a crucial component of surgical recovery for any patient, but there are unique concerns and challenges that comes with managing pain in pediatric patients. While the inherent vulnerability of children is deeply felt by clinicians and families alike, postoperative pain is nevertheless undertreated in this population. A major driver of this issue is that many common procedures require little time in the hospital, leaving most of the recovery to happen at home. Neglected postoperative pain creates short-term issues of sleep disruption, stress, and delayed recovery but can also lead to serious long-term consequences, or even disability.[1]

A central barrier to effectively managing pain following a procedure is reliably assessing a young patient’s pain level. [2] This task most often falls to parents and other caregivers, and is deceptively difficult. Research reveals two major challenges in pain reporting: a child’s ability to identify and name their pain, and the reliability of measurement instruments to consistently guide a clinical response. Opioids are unsurprisingly a central tool for effectively treating pain in children. For acute pain in particular, they are a powerful option that can provide uniquely immediate relief. However, there are known risks and side effects associated with opioid use that merit heightened caution when opiods are being used by children. Understanding a child’s experience of pain is key to responding with the appropriate balance of analgesics, and this can place an immense amount of pressure on caregivers at home.

Self-reporting is the clinically preferred way to assess pain in children.[3] However, this approach to pain measurement can be difficult among children for reasons that any parent could probably guess—a child may be afraid that sharing about their pain will cause them to return to the hospital, for example, or their social environment might cause them to overstate or understate their pain level. Commonly used instruments take various forms of visual and verbal rating scales, but the evidence is mixed on which tools are the most rigorous and widely applicable across the many developmental stages of childhood.

 

 

What if there was a way to solicit a child’s experience of pain through a familiar, less clinical and engaging instrument? This is where the recently developed Panda pain management mobile app is poised to make a difference.  The Panda app seeks to improve pediatric postoperative pain management through an engaging and easy-to-use platform. Parents or other caregivers use the app’s walkthrough design to assess and record important aspects of their child’s pain. Then, the app guides them in making decisions about when and how to administer pain medication, tracking when medication is administered to keep families on schedule for future dosages. Users receive medication alerts directly from their phone, much like the many apps families already use to schedule and track their commitments and routines.

Panda was developed by researchers at the University of British Columbia and has already seen promising results in the controlled setting of in-hospital use. Parents piloting the app with the guidance of clinical staff reported that the app was easy to use and could see themselves using it in the home setting.[4] The app is currently being evaluated for in-home use.

Providing families with an easy-to-navigate tool to not just identify pain in children following surgery, but also connect those pain measurements to a medication schedule, could be an important step in better addressing this neglected area of pain management. Pain management is a complex aspect of clinical care for patients regardless of age, and the special concerns of pediatric patients demand innovation beyond merely adapting adult guidelines for younger patients. There are exciting possibilities for the use of smartphone apps like Panda in better describing and alleviating pain in children. Tools that fit neatly into a familiar routine, like a smartphone app, may reduce some of the stress parents face in managing complex pain without clinical support.

[1] Porter FL, Grunau RE, Anand KJ: Long-term effects of pain in infants. J Dev Behav Pediatr 1999; 20:253–61Porter, FL Grunau, RE Anand, KJ

[2] Chou, Roger, et al. “Management of Postoperative Pain: a clinical practice guideline from the American pain society, the American Society of Regional Anesthesia and Pain Medicine, and the American Society of Anesthesiologists’ committee on regional anesthesia, executive committee, and administrative council.” The Journal of Pain 17.2 (2016): 131-157.

[3] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3983412/

[4] https://www.researchgate.net/publication/316853501_Feasibility_of_Panda_a_Smartphone_Application_Designed_to_Support_Pediatric_Postoperative_Pain_Management_at_Home

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Benchmarking in Anesthesia Services

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Unlike in many areas of medical practice, measuring the relevant outcomes to assess performance in anesthesia services is difficult. Assessments of anesthesia services are impeded by additional levels of complexity aside from those that already affect benchmarking in other specialties. These difficulties largely lead to imprecision, as some of the methods used to assess effectiveness lack granularity and consistency.

Because  anesthesia services are heavily entwined in the work of the surgeon, attributing a particular outcome to differences in quality of anesthesia services is difficult. That is, a systematic decline in perioperative outcomes could just as easily be attributed to a decline in performance during surgery as to a decline in performance of anesthesia team. The two are too tightly intertwined to measure varying outcomes under a constant level of surgical quality but varying anesthesia quality.

In addition to the noise that surgery introduces into an outcomes-focused evaluation of anesthesia, measuring mortality and serious morbidity as an indicator of anesthesia effectiveness is an intrinsically imprecise method. The rates of both mortality and serious morbidity that are tied to the use of anesthesia have fallen to such low levels that tracking them will only identify the very worst cases, and in fact will tend to identify cases where some exogenous event is responsible for the outcome.

A lack of serious review has stalled an empirically-informed consensus on benchmarking in anesthesia services. A study conducted by Guy Haller and his colleagues examined a total of 108 indicators used to measure the clinical effectiveness of anesthesiology, 57% of which were derived from outcomes of care while most of the remainder (42%) were derived from the process of administering care. Of those 108 indicators, only 40% had been validated by any measure beyond an expert opinion.

Prior to Haller’s and his colleagues’ study, no review had been conducted specifically intended to evaluate indicators of effectiveness for anesthesia services. The most closely related study published prior to this point identified 37 indicators of effectiveness among 27 articles, but of those only 2 such indicators related directly to anesthesia. Furthermore, those two indicators focused on outcomes, specifically death and other adverse events after surgery, which are inherently imprecise ways to assess the effectiveness of anesthesia.

At least in part, this dearth of systematic research examining indicators of effectiveness for anesthesia stems from a mismatch between the methods of academic research and the tendencies of healthcare providers in codifying their metrics. Most studies of benchmarking in healthcare have drawn upon the published literature on the subject, while Haller’s study utilized user’s manuals, web sites, and other such resources in order to gather a set of indicators that they could review. Measures of effectiveness for anesthesia tend to be described in these types of references rather than academic research.

Inconsistencies in how different providers characterize quality of anesthesia also creates challenges in constructing valid benchmarks. Some research groups will report adverse events only when they warrant a legal or disciplinary remedy, while others characterize adverse outcomes as events that result in mortality or severe morbidity. This inconsistency between research groups compounds the challenges of using outcome-focused metrics to assess the quality of services.

Additionally, different research groups define the perioperative period differently. Some such groups consider the perioperative period to terminate once the patient is discharged, while others consider the period to continue for 24 hours or more after the fact. This variation introduces further uncertainty into the benchmarking process by complicating the relationship between anesthesia and surgery. Not only are the two interlinked, but the exact nature of that linkage is not consistently documented across research groups.

Furthermore, the collection of data is inconsistent across research groups. Some rely upon voluntary reporting of outcomes while others conduct studies that examine all incidents within a given interval of time. This discrepancy introduces the potential for bias, in particular for an asymmetric bias, as voluntary reporting of incidents is likely to be systematically different from exhaustive studies over a finite period of time.

The challenges of measuring anesthesia effectiveness suggest that more process-focused indicators are needed to reliably benchmark this type of care and more robust studies are needed to validate those indicators.

References

https://academic.oup.com/bja/article/109/1/80/236985#2749964

https://academic.oup.com/bja/article/85/2/271/1745481#91202663

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Preventing and Managing Postoperative Delirium

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Delirium, a fluctuating state of inattention and disorganized thinking, is often seen in elderly hospitalized patients and the critically ill. It is distinct from emergence delirium, a separate phenomenon seen in young children emerging from anesthesia, which will not be discussed here. Postoperative delirium in the adult population carries a significant burden of morbidity, and its prevention and treatment should be familiar to anesthesia providers.

Identifying patient risk factors is important to stratify who may be at risk for postoperative delirium. Preoperative factors include advanced age, disease severity, previous neurological disease (e.g. Alzheimer’s), preexisting psychiatric disease, and substance abuse. Intraoperative factors include surgical complexity, anesthetic medications and dosages, and large variations in blood pressure, temperature, and hematocrit. Postoperative causes include certain medications (benzodiazepines are notorious for causing delirium), sleep deprivation, immobility, and prolonged ICU and/or mechanical ventilation duration.

Several standardized scoring methods have been validated in both intubated and non-intubated patients, primarily in the ICU, however they are seldom used by practitioners. Among these scoring systems, the confusion assessment method for the ICU (CAM-ICU) and the Intensive Care Delirium Screening Checklist (ICDSC) have been deemed the most valid when compared to the gold standard (DSM-IV criteria).

Anesthesia providers can help prevent postoperative delirium by avoiding benzodiazepines in patients at risk, and using opiates judiciously (though they can cause confusion in elderly or neurologically compromised patients, they can also treat pain, a known trigger of delirium). Dexmedetomidine is a promising agent in delirium prevention, having decreased the incidence of ICU delirium when compared to sedation with propofol or midazolam infusions. A single 0.5mg/kg dose of ketamine has been shown to decrease the incidence of postoperative delirium. Titrating anesthetic dosages according to Bispectral Index (BIS) has also been shown to decrease postoperative delirium.

Prophylactic use of antipsychotic drugs has not been shown to be helpful in delirium prevention. Also lacking evidence of efficacy is the use of cholinesterase inhibitors, arising from the theory that the etiology of delirium lies in acetylcholine deficiency in the brain. Using antipsychotic agents such as haloperidol and seroquel for the treatment of delirium is currently standard of care, but there is little evidence these medications reduce either the duration or severity of symptoms. The atypical antipsychotic olanzapine, on the other hand, has shown some efficacy in decreasing the incidence of delirium. As always, QT monitoring should be observed while administering these medications.

Ultimately, nonpharmacologic measures such as patient reorientation, sleep protocols, and early mobilization are the most inexpensive and lowest risk ways to combat postoperative delirium. Implementing these measures as standard of care for at-risk patients can be an important step to decreasing the incidence and duration of postoperative delirium.

References:

Evans AS, Weiner MM, Arora RC, et al. Current approach to diagnosis and treatment of delirium after cardiac surgery. Annals of Cardiac Anaesthesia. 2016;19(2):328-337. doi:10.4103/0971-9784.179634.

Siddiqi N, Harrison JK, Clegg A, Teale EA, Young J, Taylor J, Simpkins SA. Interventions for preventing delirium in hospitalised non-ICU patients. Cochrane Database of Systematic Reviews 2016, Issue 3. Art. No.: CD005563. DOI: 10.1002/14651858.CD005563.pub3.

Update on the Perioperative Surgical Home (PSH) Model

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A year ago, we wrote on the perioperative surgical home (PSH) model, a treatment paradigm that appoints the anesthesiologist as the main coordinator of care from the preoperative to perioperative phase. To summarize, the PSH model is utilized to increase efficiency and minimize cost throughout the care continuum.

Academic and research sites such as UC Irvine and University of Alabama were early adopters of the PSH model. In 2014, UC Irvine concluded a clinical study on the impact of PSH within primary joint replacement surgery practices. Researchers found that patients treated within the PSH model were associated with lowered readmission rates throughout 30 days post-surgery to statistical significance[1]. University of Alabama corroborated these results with their early stage study, finding that integration of PSH resulted in lower healthcare costs and increased efficiency in allocation of resources after the integration of PSH[2].

PSH

 

Given the positive results of early PSH studies, other academic institutions have sought to explore PSH in practical settings. For example, physician researchers at the Ochsner Health System in Louisiana recently developed a modified PSH model. In the Ochsner PSH model, anesthesiologists and orthopedic specialists worked in tandem as the main coordinators of care for patient subgroups[3]. To facilitate communication between each therapeutic side, specialists met weekly to discuss the patient pathway. By the end of treatment, Ochsner researchers found that the PSH treatment group was associated with improved outcomes and lowered costs, with an approximate savings of several hundred dollars per patient. Furthermore, patients in the PSH model did not present with worse clinical outcomes than control. In sum, the Ochsner study presents a strong case for the PSH model as a potential lever for increasing quality of care on a patient basis, while also pushing up value and driving costs down.

In multiple instances of the PSH model, anesthesiologists have served as physician leaders to great success. With expertise in multiple domains of the patient experience, anesthesiologists are strong candidates to remain at the forefront of the integration of the PSH model nationwide. As the healthcare field moves towards an emphasis on value-based care, anesthesiologists will prove to be essential leaders in PSH model applications as well as further optimized models of care.

Update: The American Academy of Physical Medicine and Rehabilitation officially endorsed the PSH model on October 12, 2017[4], further supporting the increased importance of PSH as a viable and efficient model for integration into healthcare institutions nationwide.

[1] http://www.anesthesiology.uci.edu/psh.shtml

[2] https://bmcanesthesiol.biomedcentral.com/articles/10.1186/1471-2253-13-6

[3]http://www.anesthesiologynews.com/Clinical-Anesthesiology/Article/08-17/Perioperative-Surgical-Home-Model-Shows-Benefits-in-Primary-TKA/42089/ses=ogst?enl=true

[4]https://www.aapmr.org/news-publications/member-news/member-news-details/2017/10/12/aapm-r-endorses-the-american-society-of-anesthesiologists-(asa)-perioperative-surgical-home-

State Patient Compensation Funds for Medical Malpractice

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While many specialty providers will face a medical malpractice suit at one time in their careers, anesthesiologists are higher on the risk spectrum to be subject to a claim. Anesthesiologists continue to be subject to among the highest of medical malpractice claims, with an average paid indemnity of about $100,000.[1] There are a variety of reasons that a patient may submit a claim against an anesthesiologist, and these reasons are heightened with a dramatic event during an unsuccessful surgery: for example, an airway collapse or a drug overdose. While the most likely reason for a patient to submit a claim against an anesthesiologist is due to injury, patients may also submit claims based on actions of medical error, lack of medical information on the part of the patient, or failure to treat.[2] When considering medical malpractice in the anesthesia field, it is also necessary to consider the role and responsibilities of Certified Registered Nurse Anesthetists (CRNAs).  CRNAs are often under the supervision of anesthesiologists or surgeons, such that in an event of potential medical malpractice the supervising physician is held responsible.[3] However, national regulations do exist to address claims against CRNAs.  In most states, the typical liability limit for CRNAs are $1,000,000 per claim and $3,000,000 total.[4] CRNAs may also participate in a medical malpractice insurance plan offered by his or her associated hospital network. Overall, the medical malpractice space is more highly dependent on individual claims, as little precedent has been set for the CRNA case.

 

 

In cases of medical malpractice, including for anesthesia services, the government may step in to resolve medical malpractice claims between the patient and the physician. On a state basis, Patient Compensation Funds (PCFs) are utilized in cases of medical malpractice. Essentially, these funds grant compensation to patients and/or patient’s families that have fallen subject to a medical error or omission by a physician that is in the associated state PCF system. The state then agrees to allocate and manage the PCF by collecting insurer surcharges and enrolling physicians in the fund. The state also designs specific methods to decide how and to whom the PCF can be granted.[5] For example, New Mexico, Nebraska, Wisconsin, and Indiana have specific caps in place to restrict the individual or total award amounts granted by the Fund. More qualitative regulations may also be in place, such as in South Carolina, which requires the patient to receive specific medical information about the course of the case before formally pursuing the claim. Ideally, PCFs act as an important middleman, providing for the effective release of compensation to the patient while also minimizing social and financial impact on the provider. PCFs are therefore an effective tool to continue improving the paradigm of care in the healthcare system, thus ensuring that patients are provided for, and that physicians are encouraged to continue to raise the standard of care.

[1] https://www.insurance.wa.gov/sites/default/files/documents/2016-med-mal-annual-report.pdf

[2]  http://www.medscape.com/features/slideshow/malpractice-report-2015/

[3] https://biotech.law.lsu.edu/books/lbb/x943.htm

[4] http://www.aana.com/insurance/Documents/understanding-crna-malpractice-insurance101-practice.pdf

[5] http://via.library.depaul.edu/cgi/viewcontent.cgi?article=1396&context=law-review